Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method of encoding three-dimensional points includes: re-ordering, in a re-ordered data order, pieces of attribute information of the three-dimensional points arranged in a predetermined order; encoding the pieces of attribute information re-ordered in the re-ordering, in accordance with the re-ordered data order; and generating a bitstream including (i) order information indicating the predetermined order and (ii) the pieces of attribute information encoded in the encoding.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2020/007869 flied on Feb. 26, 2020,claiming the benefit of priority of U.S. Patent Application No.62/810,621 filed on Feb. 26, 2019, the entire contents of which arehereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, snapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point group in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point group are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point groupnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include Moving Picture ExpertsGroup 4 Advanced Video Coding (MPEG-4 AVC) and High Efficiency VideoCoding (HEVC) standardized MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle is known (for example, seeInternational Publication WO 2014/020663).

SUMMARY

There has been a demand for improving encoding efficiency inthree-dimensional data encoding.

The present disclosure has an object to provide a three-dimensional dataencoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of improving encoding efficiency.

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method of encoding three-dimensionalpoints includes: re-ordering, in a re-ordered data order, pieces ofattribute information of the three-dimensional points arranged in apredetermined order; encoding the pieces of attribute informationre-ordered in the re-ordering, in accordance with the re-ordered dataorder; and generating a bitstream including (i) order informationindicating the predetermined order and (ii) the pieces of attributeinformation encoded in the encoding.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method of decoding three-dimensionalpoints encoded includes: obtaining a bitstream including (i) pieces ofattribute information of the three-dimensional points encoded and (ii)order information indicating a predetermined order, thethree-dimensional points encoded being generated by (i) re-ordering, ina re-ordered order, the pieces of attribute information ofthree-dimensional points not yet encoded and arranged in thepredetermined order and (ii) encoding, in accordance with the re-ordereddata order, the pieces of attribute information re-ordered in there-ordering; and decoding the pieces of attribute information of thethree-dimensional points encoded, in accordance with the re-ordered dataorder.

The present disclosure can provide a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of improving encoding efficiency.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosurebecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram showing the structure of encoded three-dimensionaldata according to Embodiment 1;

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a group of spaces (GOS)according to Embodiment 1;

FIG. 3 is a diagram showing an example of prediction structures amonglayers according to Embodiment 1;

FIG. 4 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 5 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 6 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 1;

FIG. 7 is a flowchart of encoding processes according to Embodiment 1;

FIG. 8 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 1;

FIG. 9 is a flowchart of decoding processes according to Embodiment 1;

FIG. 10 is a diagram showing an example of meta information according toEmbodiment 1;

FIG. 11 is a diagram showing an example structure of a sparse world(SWLD) according to Embodiment 2;

FIG. 12 is a diagram showing example operations performed by a serverand a client according to Embodiment 2;

FIG. 13 is a diagram showing example operations performed by the serverand a client according to Embodiment 2;

FIG. 14 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 15 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 16 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 2;

FIG. 17 is a flowchart of encoding processes according to Embodiment 2;

FIG. 18 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 2;

FIG. 19 is a flowchart of decoding processes according to Embodiment 2;

FIG. 20 is a diagram showing an example structure of a world (WLD)according to Embodiment 2;

FIG. 21 is a diagram showing an example octree structure of the WLDaccording to Embodiment 2;

FIG. 22 is a diagram showing an example structure of a SWLD according toEmbodiment 2;

FIG. 23 is a diagram showing an example octree structure of the SWLDaccording to Embodiment 2;

FIG. 24 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 3;

FIG. 25 is a block diagram of a three-dimensional data transmissiondevice according to Embodiment 3;

FIG. 26 is a block diagram of a three-dimensional information processingdevice according to Embodiment 4;

FIG. 27 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 5;

FIG. 28 is a diagram showing a structure of a system according toEmbodiment 6;

FIG. 29 is a block diagram of a client device according to Embodiment 6;

FIG. 30 is a block diagram of a server according to Embodiment 6;

FIG. 31 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 6;

FIG. 32 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 6;

FIG. 33 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 6;

FIG. 34 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 6;

FIG. 35 is a diagram showing a structure of a variation of the systemaccording to Embodiment 6;

FIG. 36 is a diagram showing a structure of the server and clientdevices according to Embodiment 6;

FIG. 37 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 7;

FIG. 38 is a diagram showing an example of a prediction residualaccording to Embodiment 7;

FIG. 39 is a diagram showing an example of a volume according toEmbodiment 7;

FIG. 40 is a diagram showing an example of an octree representation ofthe volume according to Embodiment 7;

FIG. 41 is a diagram showing an example of bit sequences of the volumeaccording to Embodiment 7;

FIG. 42 is a diagram showing an example of an octree representation of avolume according to Embodiment 7;

FIG. 43 is a diagram showing an example of the volume according toEmbodiment 7;

FIG. 44 is a diagram for describing an intra prediction processaccording to Embodiment 7;

FIG. 45 is a diagram for describing a rotation and translation processaccording to Embodiment 7;

FIG. 46 is a diagram showing an example syntax of an RT flag and RTinformation according to Embodiment 7;

FIG. 47 is a diagram for describing an inter prediction processaccording to Embodiment 7;

FIG. 48 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 7;

FIG. 49 is a flowchart of a three-dimensional data encoding processperformed by the three-dimensional data encoding device according toEmbodiment 7;

FIG. 50 is a flowchart of a three-dimensional data decoding processperformed by the three-dimensional data decoding device according toEmbodiment 7;

FIG. 51 is a diagram illustrating an example of three-dimensional pointsaccording to Embodiment 8;

FIG. 52 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 53 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values according to Embodiment 8;

FIG. 55 is a diagram illustrating examples of exponential-Golomb codesaccording to Embodiment 8;

FIG. 56 is a diagram indicating a process on exponential-Golomb codesaccording to Embodiment 8;

FIG. 57 is a diagram indicating an example of a syntax in attributeheader according to Embodiment 8;

FIG. 58 is a diagram indicating an example of a syntax in attribute dataaccording to Embodiment 8;

FIG. 59 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 60 is a flowchart of an attribute information encoding processaccording to Embodiment 8;

FIG. 61 is a diagram indicating processing on exponential-Golomb codesaccording to Embodiment 8;

FIG. 62 is a diagram indicating an example of a reverse lookup tableindicating relationships between remaining codes and the values thereofaccording to Embodiment 8;

FIG. 63 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 64 is a flowchart of an attribute information decoding processaccording to Embodiment 8;

FIG. 65 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 8;

FIG. 66 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 8;

FIG. 67 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 68 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in prediction modes according to Embodiment9;

FIG. 70 is a diagram showing examples of attribute information items(pieces of attribute information) used as the predicted values accordingto Embodiment 9;

FIG. 71 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 74 is a diagram for describing the encoding of the attributeinformation by using a Region Adaptive Hierarchical Transform (RAHT)according to Embodiment 10;

FIG. 75 is a diagram showing an example of setting a quantization scalefor each hierarchy according to Embodiment 10;

FIG. 76 is a diagram showing an example of a first code sequence and asecond code sequence according to Embodiment 10;

FIG. 77 is a diagram showing an example of a truncated unary codeaccording to Embodiment 10;

FIG. 78 is a diagram for describing the inverse Haar conversionaccording to Embodiment 10;

FIG. 79 is a diagram showing a syntax example of the attributeinformation according to Embodiment 10;

FIG. 80 is a diagram showing an example of a coding coefficient andZeroCnt according to Embodiment 10;

FIG. 81 is a flowchart of the three-dimensional data encoding processingaccording to Embodiment 10;

FIG. 82 is a flowchart of the attribute information encoding processingaccording to Embodiment 10;

FIG. 83 is a flowchart of the coding coefficient encoding processingaccording to Embodiment 10;

FIG. 84 is a flowchart of the three-dimensional data decoding processingaccording to Embodiment 10;

FIG. 85 is a flowchart of the attribute information decoding processingaccording to Embodiment 10;

FIG. 86 is a diagram for illustrating a schematic configuration of anattribute information encoder according to Embodiment 11;

FIG. 87 is a diagram for illustrating a schematic configuration of anattribute information decoder according to Embodiment 11;

FIG. 88 is a diagram for illustrating a schematic configuration of anattribute information encoder according to a variation of Embodiment 11;

FIG. 89 is a diagram for illustrating a schematic configuration of anattribute information decoder according to a variation of Embodiment 11;

FIG. 90 is a diagram for illustrating a re-ordering process according toEmbodiment 11;

FIG. 91 is a diagram for illustrating a first example of atransformation process for attribute information according to Embodiment11;

FIG. 92 is a diagram for illustrating a second example of thetransformation process for attribute information according to Embodiment11;

FIG. 93 is a diagram for illustrating a third example of thetransformation process for attribute information according to Embodiment11;

FIG. 94 is a diagram for illustrating a fourth example of thetransformation process for attribute information according to Embodiment11;

FIG. 95 is a diagram for illustrating a fifth example of thetransformation process for attribute information according to Embodiment11;

FIG. 96 is a diagram for illustrating examples of a connection betweenvoxels and normal vectors in the examples according to Embodiment 11;

FIG. 97 is a diagram for illustrating a sixth example of thetransformation process for attribute information according to Embodiment11;

FIG. 98 is a diagram for illustrating a seventh example of thetransformation process for attribute information according to Embodiment11;

FIG. 99 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 11;

FIG. 100 is a block diagram of an attribute information encoderaccording to Embodiment 11;

FIG. 101 is a block diagram of a point cloud re-ordering unit accordingto Embodiment 11;

FIG. 102 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 11;

FIG. 103 is a block diagram of an attribute information decoderaccording to Embodiment 11;

FIG. 104 is a block diagram of a three-dimensional data encoding deviceaccording to a variation of Embodiment 11;

FIG. 105 is a block diagram of an attribute information encoderaccording to a variation of Embodiment, 11;

FIG. 106 is a block diagram of a three-dimensional data decoding deviceaccording to a variation of Embodiment 11;

FIG. 107 is a block diagram of an attribute information decoderaccording to a variation of Embodiment 11;

FIG. 108 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 11;

FIG. 109 is a flowchart of an attribute information encoding processaccording to Embodiment 11;

FIG. 110 is a flowchart of an attribute information re-ordering processaccording to Embodiment 11;

FIG. 111 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 11;

FIG. 112 is a flowchart of an attribute information decoding processaccording to Embodiment 11;

FIG. 113 is a flowchart of an attribute information re-ordering processaccording to Embodiment 11;

FIG. 114 is a flowchart of an encoding process according to Embodiment11; and

FIG. 115 is a flowchart of a decoding process according to Embodiment11.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method of encoding three-dimensionalpoints includes: re-ordering, in a re-ordered data order, pieces ofattribute information of the three-dimensional points arranged in apredetermined order; encoding the pieces of attribute informationre-ordered in the re-ordering, in accordance with the re-ordered dataorder; and generating a bitstream including (i) order informationindicating the predetermined order and (ii) the pieces of attributeinformation encoded in the encoding.

With such a configuration, for example, when pieces of attributeinformation neighboring in the data sequence are encoded based on thedifferences between the values indicated by the neighboring pieces ofattribute information, the difference can be reduced by modifying theorder of the pieces of attribute information on the plurality ofthree-dimensional points so that the pieces of attribute informationindicating close values are adjacent to each other before encoding thepieces of attribute information on the plurality of three-dimensionalpoints. Therefore, according to this method, the coding efficiency canbe improved.

Furthermore, for example, the re-ordering may include: calculating adistance between the three-dimensional points in accordance with piecesof geometry information each included in a corresponding one of thethree-dimensional points, and the re-ordering is performed in accordancewith the distance calculated in the calculating.

When the attribute information is a value that indicates color, forexample, attribute information on a three-dimensional point is likely tohave a value that is closer to the value of attribute information onanother three-dimensional point located near the three-dimensional pointthan to the value of attribute information on another three-dimensionalpoint located far from the three-dimensional point. Therefore, with sucha configuration, the coding efficiency can be further improved.

Furthermore, for example, the re-ordering may further include:determining a reference point from among the three-dimensional points;and changing the predetermined order to the re-ordered data order inwhich a three-dimensional point having the distance shortest to thereference point among k three-dimensional points counted from thereference point to a k-th three-dimensional point in the predeterminedorder is at a position next to the reference point, k being an integergreater than or equal to 2.

With such a configuration, by properly setting k, the coding efficiencycan be improved without comparing the distances between a vast number ofthree-dimensional points, that is, without increasing the processingamount.

Furthermore, for example, the three-dimensional data encoding method mayfurther include: ordering the pieces of attribute information of thethree-dimensional points to a Morton order that is the predeterminedorder, in accordance with the pieces of geometry information included inthe three-dimensional points, wherein in the determining of thereference point in the re-ordering, a three-dimensional point having asmallest value of a Morton code among the three-dimensional points isdetermined as the reference point.

With such a configuration, the order of the pieces of attributeinformation on a plurality of three-dimensional points can be modifiedto a Morton order based on the geometry information on thethree-dimensional points, thereby properly re-ordering the pieces ofattribute information on the three-dimensional points.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method of decoding three-dimensionalpoints encoded includes: obtaining a bitstream including (i) pieces ofattribute information of the three-dimensional points encoded and (ii)order information indicating a predetermined order, thethree-dimensional points encoded being generated by (i) re-ordering, ina re-ordered order, the pieces of attribute information ofthree-dimensional points not yet encoded and arranged in thepredetermined order and (ii) encoding, in accordance with the re-ordereddata order, the pieces of attribute information re-ordered in there-ordering; and decoding the pieces of attribute information of thethree-dimensional points encoded, in accordance with the re-ordered dataorder.

With such a configuration, the attribute information on thethree-dimensional points encoded with improved coding efficiency can beproperly decoded.

Furthermore, for example, the three-dimensional data decoding method mayfurther include: ordering the pieces of attribute information decoded inthe decoding and arranged in the re-ordered data order to thepredetermined order in accordance with the order information; andoutputting the pieces of attribute information ordered to thepredetermined order in the ordering.

With such a configuration, the data order of the decoded pieces ofattribute information on the three-dimensional points can be modified tothe data order of the pieces of attribute information on thethree-dimensional points before the encoding and decoding based on theorder information, for example. Therefore, for example, equipment thathas obtained the pieces of attribute information on thethree-dimensional points decoded in the three-dimensional data decodingmethod can handle the pieces of attribute information arranged in thesame data order as those before the encoding and decoding.

Furthermore, for example, the ordering may include: calculating adistance between the three-dimensional points in accordance with piecesof geometry information each included in a corresponding one of thethree-dimensional points, and the ordering is performed in accordancewith the distance calculated in the calculating.

When the attribute information is a value that indicates color, forexample, attribute information on a three-dimensional point is likely tohave a value that is closer to the value of attribute information onanother three-dimensional point located near the three-dimensional pointthan to the value of attribute information on another three-dimensionalpoint located far from the three-dimensional point. Therefore, thecoding efficiency can be further improved by modifying the data orderbased on the distances between the three-dimensional points and encodingthe pieces of attribute information on the three-dimensional points inthe modified data order. That is, with such a configuration, pieces ofattribute information on three-dimensional points encoded with furtherimproved coding efficiency can be properly decoded.

Furthermore, for example, in the ordering, the pieces of attributeinformation of the three-dimensional points may be changed from there-ordered order to a Morton order that is the predetermined order, inaccordance with pieces of geometry information included in thethree-dimensional points.

With such a configuration, the pieces of attribute information on thethree-dimensional points can be properly arranged by re-ordering thepieces of attribute information on the three-dimensional points in theMorton order based on the geometry information on the three-dimensionalpoints.

In accordance with still another aspect of the present disclosure, athree-dimensional data encoding device that encodes three-dimensionalpoints includes: a processor; and memory wherein using the memory, theprocessor: re-orders, in a re-ordered data order, pieces of attributeinformation of the three-dimensional points arranged in a predeterminedorder; encodes the pieces of attribute information re-ordered in there-ordering, in accordance with the re-ordered data order; and generatesa bitstream including (i) order information indicating the predeterminedorder and (ii) the pieces of attribute information encoded in theencoding.

With such a configuration, when the three-dimensional data encodingdevice encodes pieces of attribute information neighboring in the datasequence based on the differences between the values indicated by theneighboring pieces of attribute information, the three-dimensional dataencoding device can reduce the difference by modifying the order of thepieces of attribute information on the plurality of three-dimensionalpoints so that the pieces of attribute information indicating closevalues are adjacent to each other before encoding the pieces ofattribute information on the plurality of three-dimensional points.Therefore, the three-dimensional data encoding device according to thepresent disclosure can improve the coding efficiency.

In accordance with still another aspect of the present disclosure, athree-dimensional data decoding device that decodes three-dimensionalpoints includes: a processor; and memory wherein using the memory, theprocessor: obtains a bitstream including (i) pieces of attributeinformation of the three-dimensional points encoded and (ii) orderinformation indicating a predetermined order, the three-dimensionalpoints encoded being generated by (i) re-ordering, in a re-orderedorder, the pieces of attribute information of three-dimensional pointsnot yet encoded and arranged in the predetermined order and (ii)encoding, in accordance with the re-ordered data order, the pieces ofattribute information re-ordered in the re-ordering; and decodes thepieces of attribute information of the three-dimensional points encoded,in accordance with the re-ordered data order.

With such a configuration, the three-dimensional data decoding devicecan properly decode the attribute information on the three-dimensionalpoints encoded with improved coding efficiency.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a Compact DiscRead Only Memory (CD-ROM), or may be implemented as any combination of asystem, a method, an integrated circuit, a computer program, and arecording medium.

The following describes embodiments with reference to the drawings. Itis to be noted that the following embodiments indicate exemplaryembodiments of the present disclosure. The numerical values, shapes,materials, constituent elements, the arrangement and connection of theconstituent elements, steps, the processing order of the steps, etc.indicated in the following embodiments are mere examples, and thus arenot intended to limit the present disclosure. Of the constituentelements described in the following embodiments, constituent elementsnot recited in any one of the independent claims that indicate thebroadest concepts will be described as optional constituent elements.

Embodiment 1

First, the data structure of encoded three-dimensional data (hereinafteralso referred to as encoded data) according to the present embodimentwill be described. FIG. 1 is a diagram showing the structure of encodedthree-dimensional data according to the present embodiment.

In the present embodiment, a three-dimensional space is divided intospaces (SPCs), which correspond to pictures in moving picture encoding,and the three-dimensional data is encoded on a SPC-by-SPC basis. EachSPC is further divided into volumes (VLMs), which correspond tomacroblocks, etc. in moving picture encoding, and predictions andtransforms are performed on a VLM-by-VLM basis. Each volume includes aplurality of voxels (VXLs), each being a minimum unit in which positioncoordinates are associated. Note that prediction is a process ofgenerating predictive three-dimensional data analogous to a currentprocessing unit by referring to another processing unit, and encoding adifferential between the predictive three-dimensional data and thecurrent processing unit, as in the case of predictions performed ontwo-dimensional images. Such prediction includes not only spatialprediction in which another prediction unit corresponding to the sametime is referred to, but also temporal prediction in which a predictionunit corresponding to a different time is referred to.

When encoding a three-dimensional space represented by point group datasuch as a point cloud, for example, the three-dimensional data encodingdevice (hereinafter also referred to as the encoding device) encodes thepoints in the point group or points included in the respective voxels ina collective manner, in accordance with a voxel size. Finer voxelsenable a highly-precise representation of the three-dimensional shape ofa point group, while larger voxels enable a rough representation of thethree-dimensional shape of a point group.

Note that the following describes the case where three-dimensional datais a point cloud, but three-dimensional data is not limited to a pointcloud, and thus three-dimensional data of any format may be employed.

Also note that voxels with a hierarchical structure may be used. In sucha case, when the hierarchy includes n levels, whether a sampling pointis included in the n−1th level or lower levels (levels below the n-thlevel) may be sequentially indicated. For example, when only the n-thlevel is decoded, and the n−1th level or lower levels include a samplingpoint, the n-th level can be decoded on the assumption that a samplingpoint is included at the center of a voxel in the n-th level.

Also, the encoding device obtains point group data, using, for example,a distance sensor, a stereo camera, a monocular camera, a gyroscopesensor, or an inertial sensor.

As in the case of moving picture encoding, each SPC is classified intoone of at least the three prediction structures that include: intra(I-SPC , which is individually decodable; predictive SPC (P-SPC) capableof only a unidirectional reference; and bidirectional SPC (B-SPC)capable of bidirectional references. Each SPC includes two types of timeinformation; decoding time and display time.

Furthermore, as shown in FIG. 1, a processing unit that includes aplurality of SPCs is a group of spaces (GOS), which is a random accessunit. Also, a processing unit that includes a plurality of GOSs is aworld (WLD).

The spatial region occupied by each world is associated with an absoluteposition on earth, by use of, for example, GPS, or latitude andlongitude information. Such position information is stored asmeta-information. Note that meta-information may be included in encodeddata, or may be transmitted separately from the encoded data.

Also, inside a GOS, all SPCs may be three-dimensionally adjacent to oneanother, or there may be a SPC that is not three-dimensionally adjacentto another SPC.

Note that the following also describes processes such as encoding,decoding, and reference to be performed on three-dimensional dataincluded in processing units such as GOS, SPC, and VLM, simply asperforming encoding/to encode, decoding/to decode, referring to, etc. ona processing unit. Also note that three-dimensional data included in aprocessing unit includes, for example, at least one pair of a spatialposition such as three-dimensional coordinates and an attribute valuesuch as color information.

Next, the prediction structures among SPCs in a GOS will be described. Aplurality of SPCs in the same GOS or a plurality of VLMs in the same SPCoccupy mutually different spaces, while having the same time information(the decoding time and the display time).

A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSscome in two types: closed GOS and open GOS. A closed GOS is a GOS inwhich all SPCs in the GOS are decodable when decoding starts from thefirst I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS isreferred to in one or more SPCs preceding the first I-SPC in the GOS inthe display time, and thus cannot be singly decoded.

Note that in the case of encoded data of map information, for example, aWLD is sometimes decoded in the backward direction, which is opposite tothe encoding order, and thus backward reproduction is difficult whenGOSs are interdependent. In such a case, a closed GOS is basically used.

Each GOS has a layer structure in height direction, and SPCs aresequentially encoded or decoded from SPCs in the bottom layer.

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS. FIG. 3 is a diagramshowing an example of prediction structures among layers.

A GOS includes at least one I-SPC. Of the objects in a three-dimensionalspace, such as a person, an animal, a car, a bicycle, a signal, and abuilding serving as a landmark, a small-sized object is especiallyeffective when encoded as an I-SPC. When decoding a GOS at a lowthroughput or at a high speed, for example, the three-dimensional datadecoding device (hereinafter also referred to as the decoding device)decodes only I-SPC(s) in the GOS.

The encoding device may also change the encoding interval or theappearance frequency of I-SPCs, depending on the degree of sparsenessand denseness of the objects in a WLD.

In the structure shown in FIG. 3, the encoding device or the decodingdevice encodes or decodes a plurality of layers sequentially from thebottom layer (layer 1). This increases the priority of data on theground and its vicinity, which involve a larger amount of information,when, for example, a self-driving car is concerned.

Regarding encoded data used for a drone, for example, encoding ordecoding may be performed sequentially from SPCs in the top layer in aGOS in height direction.

The encoding device or the decoding device may also encode or decode aplurality of layers in a manner that the decoding device can have arough grasp of a GOS first, and then the resolution is graduallyincreased. The encoding device or the decoding device may performencoding or decoding in the order of layers 3, 8, 1, 9, . . , forexample.

Next, the handling of static objects and dynamic objects will bedescribed.

A three-dimensional space includes scenes or still objects such as abuilding and a road (hereinafter collectively referred to as staticobjects), and objects with motion such as a car and a person(hereinafter collectively referred to as dynamic objects). Objectdetection is separately performed by, for example, extracting keypointsfrom point cloud data, or from video of a camera such as a stereocamera. In this description, an example method of encoding a dynamicobject will be described.

A first method is a method in which a static object and a dynamic objectare encoded without distinction. A second method is a method in which adistinction is made between a static object and a dynamic object on thebasis of identification information.

For example, a GOS is used as an identification unit. In such a case, adistinction is made between a GOS that includes SPCs constituting astatic object and a GOS that includes SPCs constituting a dynamicobject, on the basis of identification information stored in the encodeddata or stored separately from the encoded data.

Alternatively, a SPC may be used as an identification unit. In such acase, a distinction is made between a SPC that includes VLMsconstituting a static object and a SPC that includes VLMs constituting adynamic object, on the basis of the identification information thusdescribed.

Alternatively, a VLM or a VXL may be used as an identification unit. Insuch a case, a distinction is made between a VLM or a VXL that includesa static object and a VLM or a VXL that includes a dynamic object, onthe basis of the identification information thus described.

The encoding device may also encode a dynamic object as at least one VLMor SPC, and may encode a VLM or a SPC including a static object and aSPC including a dynamic object as mutually different GOSs. When the GOSsize is variable depending on the size of a dynamic object, the encodingdevice separately stores the GOS size as meta-information.

The encoding device may also encode a static object and a dynamic objectseparately from each other, and may superimpose the dynamic object ontoa world constituted by static objects. In such a case, the dynamicobject is constituted by at least one SPC, and each SPC is associatedwith at least one SPC constituting the static object onto which the eachSPC is to be superimposed. Note that a dynamic object may be representednot by SPC(s) but by at least one VLM or VXL.

The encoding device may also encode a static object and a dynamic objectas mutually different streams.

The encoding device may also generate a GOS that includes at least oneSPC constituting a dynamic object. The encoding device may further setthe size of a GOS including a dynamic object (GOS_M) and the size of aGOS including a static object corresponding to the spatial region ofGOS_M at the same size (such that the same spatial region is occupied).This enables superimposition to be performed on a GOS-by-GOS basis.

SPC(s) included in another encoded GOS may be referred to in a P-SPC ora B-SPC constituting a dynamic object. In the case where the position ofa dynamic object temporally changes, and the same dynamic object isencoded as an object in a GOS corresponding to a different time,referring to SPC(s) across GOSs is effective in terms of compressionrate.

The first method and the second method may be selected in accordancewith the intended use of encoded data. When encoded three-dimensionaldata is used as a map, for example, a dynamic object is desired to beseparated, and thus the encoding device uses the second method.Meanwhile, the encoding device uses the first method when the separationof a dynamic object is not required such as in the case wherethree-dimensional data of an event such as a concert and a sports eventis encoded.

The decoding time and the display time of a GOS or a SPC are storable inencoded data or as meta-information. All static objects may have thesame time information. In such a case, the decoding device may determinethe actual decoding time and display time. Alternatively, a differentvalue may be assigned to each GOS or SPC as the decoding time, and thesame value may be assigned as the display time. Furthermore, as in thecase of the decoder model in moving picture encoding such asHypothetical Reference Decoder (HRD) compliant with HEVC, a model may beemployed that ensures that a decoder can perform decoding without failby having a buffer of a predetermined size and by reading a bitstream ata predetermined bit rate in accordance with the decoding times.

Next, the topology of GOSs in a world will be described. The coordinatesof the three-dimensional space in a world are represented by the threecoordinate axes (x axis, y axis, and z axis) that are orthogonal to oneanother. A predetermined rule set, for the encoding order of GOSsenables encoding to be performed such that spatially adjacent GOSs arecontiguous in the encoded data. In an example shown in FIG. 4, forexample, GOSs in the x and z planes are successively encoded. After thecompletion of encoding all GOSs in certain x and z planes, the value ofthe y axis is updated. Stated differently the world expands in the yaxis direction as the encoding progresses. The GOS index numbers are setin accordance with the encoding order.

Here, the three-dimensional spaces in the respective worlds arepreviously associated one-to-one with absolute geographical coordinatessuch as UPS coordinates or latitude/longitude coordinates.Alternatively, each three-dimensional space may be represented as aposition relative to a previously set reference position. The directionsof the x axis, the y axis, and the z axis in the three-dimensional spaceare represented by directional vectors that are determined on the basisof the latitudes and the longitudes, etc. Such directional vectors arestored together with the encoded data as meta-information.

GOSs have a fixed size, and the encoding device stores such size asmeta-information. The GOS size may be changed depending on, for example,whether it is an urban area or not, or whether it is inside or outsideof a room. Stated differently, the GOS size may be changed in accordancewith the amount or the attributes of objects with information values.Alternatively, in the same world, the encoding device may adaptivelychange the GOS size or the interval between I-SPCs in GOSs in accordancewith the object density, etc. For example, the encoding device sets theGOS size to smaller and the interval between I-SPCs in GOSs to shorter,as the object density is higher.

In an example shown in FIG. 5, to enable random access with a finergranularity, a GOS with a high object density is partitioned into theregions of the third to tenth GOSs. Note that the seventh to tenth GOSsare located behind the third to sixth GOSs.

Next, the structure and the operation flow of the three-dimensional dataencoding device according to the present embodiment will be described.FIG. 6 is a block diagram of three-dimensional data encoding device 100according to the present embodiment. FIG. 7 is a flowchart of an exampleoperation performed by three-dimensional data encoding device 100.

Three-dimensional data encoding device 100 shown in FIG. 6 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. Such three-dimensional data encoding device 100 includesobtainer 101, encoding region determiner 102, divider 103, and encoder104.

As shown in FIG. 7, first, obtainer 101 obtains three-dimensional data111, which is point group data (S101).

Next, encoding region determiner 102 determines a current region forencoding from among spatial regions corresponding to the obtained pointgroup data (S102). For example, in accordance with the position of auser or a vehicle, encoding region determiner 102 determines, as thecurrent region, a spatial region around such position.

Next, divider 103 divides the point group data included in the currentregion into processing units. The processing units here means units suchas GOSs and SPCs described above. The current region here correspondsto, for example, a world described above. More specifically, divider 103divides the point group data into processing units on the basis of apredetermined GOS size, or the presence/absence/size of a dynamic object(S103). Divider 103 further determines the starting position of the SPCthat comes first in the encoding order in each GOS.

Next, encoder 104 sequentially encodes a plurality of SPCs in each GOS,thereby generating encoded three-dimensional data 112 (S104).

Note that although an example is described here in which the currentregion is divided into GOSs and SPCs, after which each GOS is encoded,the processing steps are not limited to this order. For example, stepsmay be employed in which the structure of a single GOS is determined,which is followed by the encoding of such GOS, and then the structure ofthe subsequent GOS is determined.

As thus described, three-dimensional data encoding device 100 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. More specifically three-dimensional data encoding device 100divides three-dimensional data into first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, divides each of the first processing units (GOSs) intosecond processing units (SPCs), and divides each of the secondprocessing units (SPCs) into third processing units (VLMs). Each of thethird processing units (VLMs) includes at least one voxel (VXL), whichis the minimum unit in which position information is associated.

Next, three-dimensional data encoding device 100 encodes each of thefirst processing units (GOSs), thereby generating encodedthree-dimensional data 112. More specifically, three-dimensional dataencoding device 100 encodes each of the second processing units (SPCs)in each of the first processing units (GOSs). Three-dimensional dataencoding device 100 further encodes each of the third processing units(VLMs) in each of the second processing units (SPCs).

When a current first processing unit (GOS) is a closed GOS, for example,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS). Stated differentlythree-dimensional data encoding device 100 refers to no secondprocessing unit (SPC) included in a first processing unit (GOS) that isdifferent from the current first processing unit (GOS).

Meanwhile, when a current first processing unit (GOS) is an open GOS,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS) or a second processing unit(SPC) included in a first processing unit (GOS) that is different fromthe current first processing unit (GOS).

Also, three-dimensional data encoding device 100 selects, as the type ofa current second processing unit (SPC), one of the following: a firsttype (I-SPC) in which another second processing unit (SPC) is notreferred to; a second type (P-SPC) in which another single secondprocessing unit (SPC) is referred to; and a third type in which othertwo second processing units (SPC) are referred to. Three-dimensionaldata encoding device 100 encodes the current second processing unit(SPC) in accordance with the selected type.

Next, the structure and the operation flow of the three-dimensional datadecoding device according to the present embodiment will be described.FIG. 8 is a block diagram of three-dimensional data decoding device 200according to the present embodiment. FIG. 9 is a flowchart of an exampleoperation performed by three-dimensional data decoding device 200.

Three-dimensional data decoding device 200 shown in FIG. 8 decodesencoded three-dimensional data 211, thereby generating decodedthree-dimensional data 212. Encoded three-dimensional data 211 here is,for example, encoded three-dimensional data 112 generated bythree-dimensional data encoding device 100. Such three-dimensional datadecoding device 200 includes obtainer 201, decoding start GOS determiner202, decoding SPC determiner 203, and decoder 204.

First, obtainer 201 obtains encoded three-dimensional data 211 (S201).Next, decoding start GOS determiner 202 determines a current GOS fordecoding (S202). More specifically; decoding start GOS determiner 202refers to meta-information stored in encoded three-dimensional data 211or stored separately from the encoded three-dimensional data todetermine, as the current GOS, a GOS that includes a SPC correspondingto the spatial position, the object, or the time from which decoding isto start.

Next, decoding SPC determiner 203 determines the type(s) (I, P, and/orB) of SPCs to be decoded in the GOS (S203). For example, decoding SPCdeterminer 203 determines whether to (1) decode only I-SPC(s), (2) todecode I-SPC(s) and P-SPCs, or (3) to decode SPCs of all types. Notethat the present step may not be performed, when the type(s) of SPCs tobe decoded are previously determined such as when all SPCs arepreviously determined to be decoded.

Next, decoder 204 obtains an address location within encodedthree-dimensional data 211 from which a SPC that comes first in the GOSin the decoding order (the same as the encoding order) starts. Decoder204 obtains the encoded data of the first SPC from the address location,and sequentially decodes the SPCs from such first SPC (S204). Note thatthe address location is stored in the meta-information, etc.

Three-dimensional data decoding device 200 decodes decodedthree-dimensional data 212 as thus described. More specificallythree-dimensional data decoding device 200 decodes each encoded.three-dimensional data 211 of the first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, thereby generating decoded three-dimensional data 212 ofthe first processing units (GOSs). Even more specifically,three-dimensional data decoding device 200 decodes each of the secondprocessing units (SPCs) in each of the first processing units (GOSs).Three-dimensional data decoding device 200 further decodes each of thethird processing units (VLMs) in each of the second processing units(SPCs).

The following describes meta-information for random access. Suchmeta-information is generated by three-dimensional data encoding device100, and included in encoded three-dimensional data 112 (211).

In the conventional random access for a two-dimensional moving picture,decoding starts from the first frame in a random access unit that isclose to a specified time. Meanwhile, in addition to times, randomaccess to spaces (coordinates, objects, etc.) is assumed to be performedin a world.

To enable random access to at least three elements of coordinates,objects, and times, tables are prepared that associate the respectiveelements with the GOS index numbers. Furthermore, the GOS index numbersare associated with the addresses of the respective first I-SPCs in theGOSs. FIG. 10 is a diagram showing example tables included in themeta-information. Note that not all the tables shown in FIG. 10 arerequired to be used, and thus at least one of the tables is used.

The following describes an example in which random access is performedfrom coordinates as a starting point. To access the coordinates (x2, y2,and z2), the coordinates-GOS table is first referred to, which indicatesthat the point corresponding to the coordinates (x2, y2, and z2) isincluded in the second GOS. Next, the GOS-address table is referred to,which indicates that the address of the first I-SPC in the second GOS isaddr(2). As such, decoder 204 obtains data from this address to startdecoding.

Note that the addresses may either be logical addresses or physicaladdresses of an HDD or a memory. Alternatively, information thatidentifies file segments may be used instead of addresses. File segmentsare, for example, units obtained by segmenting at least one GOS, etc.

When an object spans across a plurality of GOSs, the object-GOS tablemay show a plurality of GOSs to which such object belongs. When suchplurality of GOSs are closed GOSs, the encoding device and the decodingdevice can perform encoding or decoding in parallel. Meanwhile, whensuch plurality of GOSs are open GOSs, a higher compression efficiency isachieved by the plurality of GOSs referring to each other.

Example objects include a person, an animal, a car, a bicycle, a signal,and a building serving as a landmark. For example, three-dimensionaldata encoding device 100 extracts keypoints specific to an object from athree-dimensional point cloud, etc., when encoding a world, and detectsthe object on the basis of such keypoints to set the detected object asa random access point.

As thus described, three-dimensional data encoding device 100 generatesfirst information indicating a plurality of first processing units(GOSs) and the three-dimensional coordinates associated with therespective first processing units (GOSs). Encoded three-dimensional data112 (211) includes such first information. The first information furtherindicates at least one of objects, times, and data storage locationsthat are associated with the respective first processing units (GOSs).

Three-dimensional data decoding device 200 obtains the first informationfrom encoded three-dimensional data 211. Using such first information,three-dimensional data decoding device 200 identifies encodedthree-dimensional data 211 of the first processing unit that correspondsto the specified three-dimensional coordinates, object, or time, anddecodes encoded three-dimensional data 211.

The following describes an example of other meta-information. Inaddition to the meta-information for random access, three-dimensionaldata encoding device 100 may also generate and store meta-information asdescribed below; and three-dimensional data decoding device 200 may usesuch meta-information at the time of decoding.

When three-dimensional data is used as map information, for example, aprofile is defined in accordance with the intended use, and informationindicating such profile may be included in meta-information. Forexample, a profile is defined for an urban or a suburban area, or for aflying object, and the maximum or minimum size, etc. of a world, a SPCor a VLM, etc. is defined in each profile. For example, more detailedinformation is required for an urban area than for a suburban area, andthus the minimum VLM size is set to small.

The meta-information may include tag values indicating object types.Each of such tag values is associated with VLMs, SPCs, or GOSs thatconstitute an object. For example, a tag value may be set for eachobject type in a manner, for example, that the tag value “0” indicates“person,” the tag value “1” indicates “car,” and the tag value “2”indicates “signal”. Alternatively when an object type is hard to judge,or such judgment is not required, a tag value may be used that indicatesthe size or the attribute indicating, for example, whether an object isa dynamic object or a static object.

The meta-information may also include information indicating a range ofthe spatial region occupied by a world.

The meta-information may also store the SPC or VXL size as headerinformation common to the whole stream of the encoded data or to aplurality of SPCs, such as SPCs in a GOS.

The meta-information may also include identification information on adistance sensor or a camera that has been used to generate a pointcloud, or information indicating the positional accuracy of a pointgroup in the point cloud.

The meta-information may also include information indicating whether aworld is made only of static objects or includes a dynamic object.

The following describes variations of the present embodiment.

The encoding device or the decoding device may encode or decode two ormore mutually different SPCs or GOSs in parallel. GOSs to be encoded ordecoded in parallel can be determined on the basis of meta-information,etc. indicating the spatial positions of the GOSs.

When three-dimensional data is used as a spatial map for use by a car ora flying object, etc. in traveling, or for creation of such a spatialmap, for example, the encoding device or the decoding device may encodeor decode GOSs or SPCs included in a space that is identified on thebasis of GPS information, the route information, the zoom magnification,etc.

The decoding device may also start decoding sequentially from a spacethat is close to the self-location or the traveling route. The encodingdevice or the decoding device may give a lower priority to a spacedistant from the self-location or the traveling route than the priorityof a nearby space to encode or decode such distant place. To “give alower priority” means here, for example, to lower the priority in theprocessing sequence, to decrease the resolution (to apply decimation inthe processing), or to lower the image quality (to increase the encodingefficiency by for example, setting the quantization step to larger).

When decoding encoded data that is hierarchically encoded in a space,the decoding device may decode only the bottom layer in the hierarchy.

The decoding device may also start decoding preferentially from thebottom layer of the hierarchy in accordance with the zoom magnificationor the intended use of the map.

For self-location estimation or object recognition, etc. involved in theself-driving of a car or a robot, the encoding device or the decodingdevice may encode or decode regions at a lower resolution, except for aregion that is lower than or at a specified height from the ground (theregion to be recognized).

The encoding device may also encode point clouds representing thespatial shapes of a room interior and a room exterior separately. Forexample, the separation of a GOS representing a room interior (interiorGOS) and a GOS representing a room exterior (exterior GOS) enables thedecoding device to select a GOS to be decoded in accordance with aviewpoint location, when using the encoded data.

The encoding device may also encode an interior COS and an exterior GOShaving close coordinates so that such GOSs come adjacent to each otherin an encoded stream. For example, the encoding device associates theidentifiers of such GOSs with each other, and stores informationindicating the associated identifiers into the meta-information that isstored in the encoded stream or stored separately. This enables thedecoding device to refer to the information in the meta-information toidentify an interior GOS and an exterior GOS having close coordinates.

The encoding device may also change the GOS size or the SPC sizedepending on whether a GOS is an interior GOS or an exterior GOS. Forexample, the encoding device sets the size of an interior GOS to smallerthan the size of an exterior GOS. The encoding device may also changethe accuracy of extracting keypoints from a point cloud, or the accuracyof detecting objects, for example, depending on whether a GOS is aninterior GOS or an exterior GOS.

The encoding device may also add, to encoded data, information by whichthe decoding device displays objects with a distinction between adynamic object and a static object. This enables the decoding device todisplay a dynamic object together with, for example, a red box orletters for explanation. Note that the decoding device may display onlya red box or letters for explanation, instead of a dynamic object. Thedecoding device may also display more particular object types. Forexample, a red box may be used for a car, and a yellow box may be usedfor a person.

The encoding device or the decoding device may also determine whether toencode or decode a dynamic object and a static object, as a differentSPC or GOS, in accordance with, for example, the appearance frequency ofdynamic objects or a ratio between static objects and dynamic objects.For example, when the appearance frequency or the ratio of dynamicobjects exceeds a threshold, a SPC or a GOS including a mixture of adynamic object and a static object is accepted, while when theappearance frequency or the ratio of dynamic objects is below athreshold, a SPC or GOS including a mixture of a dynamic object and astatic object is unaccepted.

When detecting a dynamic object not from a point cloud but fromtwo-dimensional image information of a camera, the encoding device mayseparately obtain information for identifying a detection result (box orletters) and the object position, and encode these items of informationas part of the encoded three-dimensional data. In such a case, thedecoding device superimposes auxiliary information (box or letters)indicating the dynamic object onto a resultant of decoding a staticobject to display it.

The encoding device may also change the sparseness and denseness of VXLsor VLMs in a SPC in accordance with the degree of complexity of theshape of a static object. For example, the encoding device sets VXLs erVLMs at a higher density as the shape of a static object is morecomplex. The encoding device may further determine a quantization step,etc. for quantizing spatial positions or color information in accordancewith the sparseness and denseness of VXLs or VLMs. For example, theencoding device sets the quantization step to smaller as the density ofVXLs or VLMs is higher.

As described above, the encoding device or the decoding device accordingto the present embodiment encodes or decodes a space on a SPC-by-SPCbasis that includes coordinate information.

Furthermore, the encoding device and the decoding device performencoding or decoding on a volume-by-volume basis in a SPC. Each volumeincludes a voxel, which is the minimum unit in which positioninformation is associated.

Also, using a table that associates the respective elements of spatialinformation including coordinates, objects, and times with GOSs or usinga table that associates these elements with each other, the encodingdevice and the decoding device associate any ones of the elements witheach other to perform encoding or decoding. The decoding device uses thevalues of the selected elements to determine the coordinates, andidentifies a volume, a voxel, or a SPC from such coordinates to decode aSPC including such volume or voxel, or the identified SPC.

Furthermore, the encoding device determines a volume, a voxel, or a SPCthat is selectable in accordance with the elements, through extractionof keypoints and object recognition, and encodes the determined volume,voxel, or SPC, as a volume, a voxel, or a SPC to which random access ispossible.

SPCs are classified into three types: I-SPC that is singly encodable ordecodable; P-SPC that is encoded or decoded by referring to any one ofthe processed SPCs; and B-SPC that is encoded or decoded by referring toany two of the processed SPCs.

At least one volume corresponds to a static object or a dynamic object.A SPC including a static object and a SPC including a dynamic object areencoded or decoded as mutually different GOSs. Stated differently, a SPCincluding a static object and a SPC including a dynamic object areassigned to different GOSs.

Dynamic objects are encoded or decoded on an object-by-object basis, andare associated with at least one SPC including a static object. Stateddifferently a plurality of dynamic objects are individually encoded, andthe obtained encoded data of the dynamic objects is associated with aSPC including a static object.

The encoding device and the decoding device give an increased priorityto I-SPC(s) in a GOS to perform encoding or decoding. For example, theencoding device performs encoding in a manner that prevents thedegradation of I-SPCs (in a manner that enables the originalthree-dimensional data to be reproduced with a higher fidelity afterdecoded). The decoding device decodes, for example, only I-SPCs.

The encoding device may change the frequency of using I-SPCs dependingon the sparseness and denseness or the number (amount) of the objects ina world to perform encoding. Stated differently; the encoding devicechanges the frequency of selecting I-SPCs depending on the number or thesparseness and denseness of the objects included in thethree-dimensional data. For example, the encoding device uses I-SPCs ata higher frequency as the density of the objects in a world is higher.

The encoding device also sets random access points on a GOS-by-GOSbasis, and stores information indicating the spatial regionscorresponding to the GOSs into the header information.

The encoding device uses, for example, a default value as the spatialsize of a GOS. Note that the encoding device may change the GOS sizedepending on the number (amount) or the sparseness and denseness ofobjects or dynamic objects. For example, the encoding device sets thespatial size of a GOS to smaller as the density of objects or dynamicobjects is higher or the number of objects or dynamic objects isgreater.

Also, each SPC or volume includes a keypoint group that is derived byuse of information obtained by a sensor such as a depth sensor, agyroscope sensor, or a camera sensor. The coordinates of the keypointsare set at the central positions of the respective voxels. Furthermore,finer voxels enable highly accurate position information.

The keypoint group is derived by use of a plurality of pictures. Aplurality of pictures include at least two types of time information:the actual time information and the same time information common to aplurality of pictures that are associated with SPCs (for example, theencoding time used for rate control, etc.).

Also, encoding or decoding is performed on a GOS-by-GOS basis thatincludes at least one SPC.

The encoding device and the decoding device predict P-SPCs or B-SPCs ina current GOS by referring to SPCs in a processed GOS.

Alternatively, the encoding device and the decoding device predictP-SPCs or B-SPCs in a current GOS, using the processed SPCs in thecurrent GOS, without referring to a different GOS.

Furthermore, the encoding device and the decoding device transmit orreceive an encoded stream on a world-by-world basis that includes atleast one GOS.

Also, a GOS has a layer structure in one direction at least in a world,and the encoding device and the decoding device start encoding ordecoding from the bottom layer. For example, a random accessible GOSbelongs to the lowermost layer. A GOS that belongs to the same layer ora lower layer is referred to in a GOS that belongs to an upper layer.Stated differently, a GOS is spatially divided in a predetermineddirection in advance to have a plurality of layers, each including atleast one SPC. The encoding device and the decoding device encode ordecode each SPC by referring to a SPC included in the same layer as theeach SPC or a SPC included in a layer lower than that of the each SPC.

Also, the encoding device and the decoding device successively encode ordecode GOSs on a world-by-world basis that includes such GOSs. In sodoing, the encoding device and the decoding device write or read outinformation indicating the order (direction) of encoding or decoding asmetadata. Stated differently, the encoded data includes informationindicating the order of encoding a plurality of GOSs.

The encoding device and the decoding device also encode or decodemutually different two or more SPCs or GOSs in parallel.

Furthermore, the encoding device and the decoding device encode ordecode the spatial information (coordinates, size, etc.) on a SPC or aGOS.

The encoding device and the decoding device encode or decode SPCs orGOSs included in an identified space that is identified on the basis ofexternal information on the self-location or/and region size, such asGPS information, route information, or magnification.

The encoding device or the decoding device gives a lower priority to aspace distant from the self-location than the priority of a nearby spaceto perform encoding or decoding.

The encoding device sets a direction at one of the directions in aworld, in accordance with the magnification or the intended use, toencode a GOS having a layer structure in such direction. Also, thedecoding device decodes a GOS having a layer structure in one of thedirections in a world that has been set in accordance with themagnification or the intended use, preferentially from the bottom layer.

The encoding device changes the accuracy of extracting keypoints, theaccuracy of recognizing objects, or the size of spatial regions, etc.included in a SPC, depending on whether an object is an interior objector an exterior object. Note that the encoding device and the decodingdevice encode or decode an interior GOS and an exterior GOS having closecoordinates in a manner that these GOSs come adjacent to each other in aworld, and associate their identifiers with each other for encoding anddecoding.

Embodiment 2

When using encoded data of a point cloud in an actual device or service,it is desirable that necessary information be transmitted/received inaccordance with the intended use to reduce the network bandwidth.However, there has been no such functionality in the structure ofencoding three-dimensional data, nor an encoding method therefor.

The present embodiment describes a three-dimensional data encodingmethod and a three-dimensional data encoding device for providing thefunctionality of transmitting/receiving only necessary information inencoded data of a three-dimensional point cloud in accordance with theintended use, as well as a three-dimensional data decoding method and athree-dimensional data decoding device for decoding such encoded data.

A voxel (VXL) with a feature greater than or equal to a given amount isdefined as a feature voxel (FVXL), and a world (WLD) constituted byFVXLs is defined as a sparse world (SWLD). FIG. 11 is a diagram showingexample structures of a sparse world and a world. A SWLD includes:FGOSs, each being a GOS constituted by FVXLs: FSPCs, each being a SPCconstituted by FVXLs; and FVLMs, each being a VLM constituted by FVXLs.The data structure and prediction structure of a FGOS, a FSPC, and aFVLM may be the same as those of a GOS, a SPC, and a VLM.

A feature represents the three-dimensional position information on a VXLor the visible-light information on the position of a VXL. A largenumber of features are detected especially at a corner, an edge, etc. ofa three-dimensional object. More specifically, such a feature is athree-dimensional feature or a visible-light feature as described below,but may be any feature that represents the position, luminance, or colorinformation, etc. on a VXL.

Used as three-dimensional features are signature of histograms oforientations (SHOT) features, point feature histograms (PFH) features,or point pair feature (PPF) features.

SHOT features are obtained by dividing the periphery of a VXL, andcalculating an inner product of the reference point and the normalvector of each divided region to represent the calculation result as ahistogram. SHOT features are characterized by a large number ofdimensions and high-level feature representation.

PFH features are obtained by selecting a large number of two point pairsin the vicinity of a VXL, and calculating the normal vector, etc. fromeach two point pair to represent the calculation result as a histogram.PFH features are histogram features, and thus are characterized byrobustness against a certain extent of disturbance and also high-levelfeature representation.

PPF features are obtained by using a normal vector, etc. for each twopoints of VXLs. PPF features, for which all VXLs are used, hasrobustness against occlusion.

Used as visible-light features are scale-invariant feature transform(SIFT), speeded up robust features (SURF), or histogram of orientedgradients (HOG), etc. that use information on an image such as luminancegradient information.

A SWLD is generated by calculating the above-described features of therespective VXLs in a WLD to extract FVXLs. Here, the SWLD may be updatedevery time the WLD is updated, or may be regularly updated after theelapse of a certain period of time, regardless of the timing at whichthe WLD is updated.

A SWLD may be generated for each type of features. For example,different SWLDs may be generated for the respective types of features,such as SWLD1 based on SHOT features and SWLD2 based on SIFT features sothat SWLDs are selectively used in accordance with the intended use.Also, the calculated feature of each FVXL may be held in each FVXL asfeature information.

Next, the usage of a sparse world (SWLD) will be described. A SWLDincludes only feature voxels (FVXLs), and thus its data size is smallerin general than that of a WLD that includes all VXLs.

In an application that utilizes features for a certain purpose, the useof information on a SWLD instead of a WLD reduces the time required toread data from a hard disk, as well as the bandwidth and the timerequired for data transfer over a network. For example, a WLD and a SWLDare held in a server as map information so that map information to besent is selected between the WLD and the SWLD in accordance with arequest from a client. This reduces the network bandwidth and the timerequired for data transfer. More specific examples will be describedbelow.

FIG. 12 and FIG. 13 are diagrams showing usage examples of a SWLD and aWLD. As FIG. 12 shows, when client 1, which is a vehicle-mounted device,requires map information to use it for self-location determination,client 1 sends to a server a request for obtaining map data forself-location estimation (S301). The server sends to client 1 the SWLDin response to the obtainment request (S302). Client 1 uses the receivedSWLD to determine the self-location (S303). In so doing, client 1obtains VXL information on the periphery of client 1 through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.Client 1 then estimates the self-location information from the obtainedVXL information and the SWLD. Here, the self-location informationincludes three-dimensional position information, orientation, etc. ofclient 1.

As FIG. 13 shows, when client 2, which is a vehicle-mounted device,requires map information to use it for rendering a map such as athree-dimensional map, client 2 sends to the server a request forobtaining map data for map rendering (S311). The server sends to client2 the WLD in response to the obtainment request (S312). Client 2 usesthe received WLD to render a map (S313). In so doing, client 2 uses, forexample, image client 2 has captured by a visible-light camera, etc. andthe WLD obtained from the server to create a rendering image, andrenders such created image onto a screen of a car navigation system,etc.

As described above, the server sends to a client a SWLD when thefeatures of the respective VXLs are mainly required such as in the caseof self-location estimation, and sends to a client a WLD when detailedVXL information is required such as in the case of map rendering. Thisallows for an efficient sending/receiving of map data.

Note that a client may self-judge which one of a SWLD and a WLD isnecessary and request the server to send a SWLD or a WLD. Also, theserver may judge which one of a SWLD and a WLD to send in accordancewith the status of the client or a network.

Next, a method will be described of switching the sending/receivingbetween a sparse world (SWLD) and a world (WLD).

Whether to receive a WLD or a SWLD may be switched in accordance withthe network bandwidth. FIG. 14 is a diagram showing an example operationin such case. For example, when a low-speed network is used that limitsthe usable network bandwidth, such as in a Long-Term Evolution (LTE)environment, a client accesses the server over a low-speed network(S321), and obtains the SWLD from the server as map information (S322).Meanwhile, when a high-speed network is used that has an adequatelybroad network bandwidth, such as in a WiFi environment, a clientaccesses the server over a high-speed network (S323), and obtains theWLD from the server (S324). This enables the client to obtainappropriate map information in accordance with the network bandwidthsuch client is using.

More specifically a client receives the SWLD over an LTE network when inoutdoors, and obtains the WLD over a WiFi network when in indoors suchas in a facility. This enables the client to obtain more detailed mapinformation on indoor environment.

As described above, a client may request for a WLD or a SWLD inaccordance with the bandwidth of a network such client is using.Alternatively, the client may send to the server information indicatingthe bandwidth of a network such client is using, and the server may sendto the client data (the WLD or the SWLD) suitable for such client inaccordance with the information. Alternatively, the server may identifythe network bandwidth the client is using, and send to the client data(the WLD or the SWLD) suitable for such client.

Also, whether to receive a WLD or a SWLD may be switched in accordancewith the speed of traveling. FIG. 15 is a diagram showing an exampleoperation in such case. For example, when traveling at a high speed(S331), a client receives the SWLD from the server (S332). Meanwhile,when traveling at a low speed (S333), the client receives the WLD fromthe server (S334). This enables the client to obtain map informationsuitable to the speed, while reducing the network bandwidth. Morespecifically, when traveling on an expressway the client receives theSWLD with a small data amount, which enables the update of rough mapinformation at an appropriate speed. Meanwhile, when traveling on ageneral road, the client receives the WLD, which enables the obtainmentof more detailed map information.

As described above, the client may request the server for a WLD or aSWLD in accordance with the traveling speed of such client.Alternatively the client may send to the server information indicatingthe traveling speed of such client, and the server may send to theclient data (the WLD or the SWLD) suitable to such client in accordancewith the information. Alternatively; the server may identify thetraveling speed of the client to send data (the WLD or the SWLD)suitable to such client.

Also, the client may obtain, from the server, a SWLD first, from whichthe client may obtain a WLD of an important region. For example, whenobtaining map information, the client first obtains a SWLD for rough mapinformation, from which the client narrows to a region in which featuressuch as buildings, signals, or persons appear at high frequency so thatthe client can later obtain a WLD of such narrowed region. This enablesthe client to obtain detailed information on a necessary region, whilereducing the amount of data received from the server.

The server may also create from a WLD different SWLDs for the respectiveobjects, and the client may receive SWLDs in accordance with theintended use. This reduces the network bandwidth. For example, theserver recognizes persons or cars in a WLD in advance, and creates aSWLD of persons and a SWLD of cars. The client, when wishing to obtaininformation on persons around the client, receives the SWLD of persons,and when wishing to obtain information on cars, receives the SWLD ofcars. Such types of SWLDs may be distinguished by information (flag, ortype, etc.) added to the header, etc.

Next, the structure and the operation flow of the three-dimensional dataencoding device (e.g., a server) according to the present embodimentwill be described. FIG. 16 is a block diagram of three-dimensional dataencoding device 400 according to the present embodiment. FIG. 17 is aflowchart of three-dimensional data encoding processes performed bythree-dimensional data encoding device 400.

Three-dimensional data encoding device 400 shown in FIG. 16 encodesinput three-dimensional data 411, thereby generating encodedthree-dimensional data 413 and encoded three-dimensional data 414, eachbeing an encoded stream. Here, encoded three-dimensional data 418 isencoded three-dimensional data corresponding to a WLD, and encodedthree-dimensional data 414 is encoded three-dimensional datacorresponding to a SWLD. Such three-dimensional data encoding device 400includes, obtainer 401, encoding region determiner 402, SWLD extractor403, WLD encoder 404, and SWLD encoder 405.

First, as FIG. 17 shows, obtainer 401 obtains input three-dimensionaldata 411, which is point group data in a three-dimensional space (S401).

Next, encoding region determiner 402 determines a current spatial regionfor encoding on the basis of a spatial region in which the point clouddata is present (S402).

Next, SWLD extractor 403 defines the current spatial region as a WLD,and calculates the feature from each VXL included in the WILD. Then,SWLD extractor 403 extracts VXLs having an amount of features greaterthan or equal to a predetermined threshold, defines the extracted VXLsas FVXLs, and adds such FVXLs to a SWLD, thereby generating extractedthree-dimensional data 412 (S403). Stated differently, extractedthree-dimensional data 412 having an amount of features greater than orequal to the threshold is extracted from input three-dimensional data411.

Next, WLD encoder 404 encodes input three-dimensional data 411corresponding to the WLD, thereby generating encoded three-dimensionaldata 413 corresponding to the WLD (S404). In so doing, WLD encoder 404adds to the header of encoded three-dimensional data 413 informationthat distinguishes that such encoded three-dimensional data 413 is astream including a WLD.

SWLD encoder 405 encodes extracted three-dimensional data 412corresponding to the SWLD, thereby generating encoded three-dimensionaldata 414 corresponding to the SWLD (S405). In so doing, SWLD encoder 405adds to the header of encoded three-dimensional data 414 informationthat distinguishes that such encoded three-dimensional data 414 is astream including a SWLD.

Note that the process of generating encoded three-dimensional data 413and the process of generating encoded three-dimensional data 414 may beperformed in the reverse order. Also note that a part or all of theseprocesses may be performed in parallel.

A parameter “world_type” is defined, for example, as information addedto each header of encoded three-dimensional data 413 and encodedthree-dimensional data 414. world_type=0 indicates that a streamincludes a. WLD, and world_type=1 indicates that a stream includes aSWLD. An increased number of values may be further assigned to define alarger number of types, e.g., world_type=2. Also, one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 mayinclude a specified flag. For example, encoded three-dimensional data414 may be assigned with a flag indicating that such stream includes aSWLD. In such a case, the decoding device can distinguish whether suchstream is a stream including a WLD or a stream including a SWLD inaccordance with the presence/absence of the flag.

Also, an encoding method used by WLD encoder 404 to encode a WLD may bedifferent from an encoding method used by SWLD encoder 405 to encode aSWLD.

For example, data of a SWLD is decimated, and thus can have a lowercorrelation with the neighboring data than that of a WLD. For thisreason, of intra prediction and inter prediction, inter prediction maybe more preferentially performed in an encoding method used for a SWLDthan in an encoding method used for a WLD.

Also, an encoding method used for a SWLD and an encoding method used fora WLD may represent three-dimensional positions differently. Forexample, three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Also, SWLD encoder 405 performs encoding in a manner that encodedthree-dimensional data 414 of a SWLD has a smaller data size than thedata size of encoded three-dimensional data 413 of a WLD. A SWLD canhave a lower inter-data correlation, for example, than that of a WLD asdescribed above. This can lead to a decreased encoding efficiency andthus to encoded three-dimensional data 414 having a larger data sizethan the data size of encoded three-dimensional data 413 of a WLD. Whenthe data size of the resulting encoded three-dimensional data 414 islarger than the data size of encoded three-dimensional data 413 of aWLD, SWLD encoder 405 performs encoding again to re-generate encodedthree-dimensional data 414 having a reduced data size.

For example, SWLD extractor 403 re-generates extracted three-dimensionaldata 412 having a reduced number of keypoints to be extracted, and SWLDencoder 405 encodes such extracted three-dimensional data 412.Alternatively, SWLD encoder 405 may perform more coarse quantization.More coarse quantization is achieved, for example, by rounding the datain the lowermost level in an octree structure described below.

When failing to decrease the data size of encoded three-dimensional data414 of the SWLD to smaller than the data size of encodedthree-dimensional data 413 of the WLD, SWLD encoder 405 may not generateencoded three-dimensional data 414 of the SWLD. Alternatively encodedthree-dimensional data 413 of the WLD may be copied as encodedthree-dimensional data 414 of the SWLD. Stated differently, encodedthree-dimensional data 413 of the WLD may be used as it is as encodedthree-dimensional data 414 of the SWLD.

Next, the structure and the operation flow of the three-dimensional datadecoding device (e.g., a client) according to the present embodimentwill be described. FIG. 18 is a block diagram of three-dimensional datadecoding device 500 according to the present embodiment. FIG. 19 is aflowchart of three-dimensional data decoding processes performed bythree-dimensional data decoding device 500.

Three-dimensional data decoding device 500 shown in FIG. 18 decodesencoded three-dimensional data 511, thereby generating decodedthree-dimensional data 512 or decoded three-dimensional data 513.Encoded three-dimensional data 511 here is, for example, encodedthree-dimensional data 413 or encoded three-dimensional data 414generated by three-dimensional data encoding device 400.

Such three-dimensional data decoding device 500 includes obtainer 501,header analyzer 502, WLD decoder 503, and SWLD decoder 504.

First, as FIG. 19 shows, obtainer 501 obtains encoded three-dimensionaldata 511 (S501). Next, header analyzer 502 analyzes the header ofencoded three-dimensional data 511 to identify whether encodedthree-dimensional data 511 is a stream including a WLD or a streamincluding a SWLD (S502). For example, the above-described parameterworld is referred to in making such identification.

When encoded three-dimensional data 511 is a stream including a WLD (Yesin S503), WLD decoder 503 decodes encoded three-dimensional data 511,thereby generating decoded three-dimensional data 512 of the WLD (S504).Meanwhile, when encoded three-dimensional data 511 is a stream includinga SWLD (No in S503), SWLD decoder 504 decodes encoded three-dimensionaldata 511, thereby generating decoded three-dimensional data 513 of theSWLD (S505).

Also, as in the case of the encoding device, a decoding method used byWLD decoder 503 to decode a WLD may be different from a decoding methodused by SWLD decoder 504 to decode a SWLD. For example, of intraprediction and inter prediction, inter prediction may be morepreferentially performed in a decoding method used for a SWLD than in adecoding method used for a WLD.

Also, a decoding method used for a SWLD and a decoding method used for aWLD may represent three-dimensional positions differently. For example,three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Next, an octree representation will be described, which is a method ofrepresenting three-dimensional positions. VXL data included inthree-dimensional data is converted into an octree structure beforeencoded. FIG. 20 is a diagram showing example VXLs in a WLD. FIG. 21 isa diagram showing an octree structure of the WLD shown in FIG. 20. Anexample shown in FIG. 20 illustrates three VXLs 1 to 3 that includepoint groups (hereinafter referred to as effective VXLs). As FIG. 21shows, the octree structure is made of nodes and leaves. Each node has amaximum of eight nodes or leaves. Each leaf has VXL information. Here,of the leaves shown in FIG. 21, leaf 1, leaf 2, and leaf 3 representVXL1, VXL2, and VXL3 shown in FIG. 20, respectively.

More specifically each node and each leaf correspond to athree-dimensional position. Node 1 corresponds to the entire block shownin FIG. 20. The block that corresponds to node 1 is divided into eightblocks. Of these eight blocks, blocks including effective VXLs are setas nodes, while the other blocks are set as leaves. Each block thatcorresponds to a node is further divided into eight nodes or leaves.These processes are repeated by the number of times that is equal to thenumber of levels in the octree structure. All blocks in the lowermostlevel are set as leaves.

FIG. 22 is a diagram showing an example SWLD generated from the WLDshown in FIG. 20. VXL1 and VXL2 shown in FIG. 20 are judged as FVXL1 andFVXL2 as a result of feature extraction, and thus are added to the SWLD.Meanwhile, VXL3 is not judged as a FVXL, and thus is not added to theSWLD. FIG. 23 is a diagram showing an octree structure of the SWLD shownin FIG. 22. In the octree structure shown in FIG. 23, leaf 3corresponding to VXL3 shown in FIG. 21 is deleted. Consequently node 3shown in FIG. 21 has lost an effective VXL, and has changed to a leaf.As described above, a SWLD has a smaller number of leaves in generalthan a WLD does, and thus the encoded three-dimensional data of the SWLDis smaller than the encoded three-dimensional data of the WLD.

The following describes variations of the present embodiment.

For self-location estimation, for example, a client, being avehicle-mounted device, etc., may receive a SWLD from the server to usesuch SWLD to estimate the self-location. Meanwhile, for obstacledetection, the client may detect obstacles by use of three-dimensionalinformation on the periphery obtained by such client through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.

In general, a SWLD is less likely to include VXL data on a flat region.As such, the server may hold a subsample world (subWLD) obtained bysubsampling a WLD for detection of static obstacles, and send to theclient the SWLD and the subWLD. This enables the client to performself-location estimation and obstacle detection on the client's part,while reducing the network bandwidth.

When the client renders three-dimensional map data at a high speed, mapinformation having a mesh structure is more useful in some cases. Assuch, the server may generate a mesh from a WLD to hold it beforehand asa mesh world (MWLD). For example, when wishing to perform coarsethree-dimensional rendering, the client receives a MWLD, and whenwishing to perform detailed three-dimensional rendering, the clientreceives a WLD. This reduces the network bandwidth.

In the above description, the server sets, as FVXLs, VXLs having anamount of features greater than or equal to the threshold, but theserver may calculate FVXLs by a different method. For example, theserver may judge that a VXL, a VLM, a SPC, or a GOS that constitutes asignal, or an intersection, etc. as necessary for self-locationestimation, driving assist, or self-driving, etc., and incorporate suchVXL, VLM, SPC, or GOS into a SWLD as a FVXL, a FVLM, a FSPC, or a FGOS.Such judgment may be made manually. Also, FVXLs, etc. that have been seton the basis of an amount of features may be added to FVXLs, etc.obtained by the above method. Stated differently, SWLD extractor 403 mayfurther extract, from input three-dimensional data 411, datacorresponding to an object having a predetermined attribute as extractedthree-dimensional data 412.

Also, that a VXL, a VLM, a SPC, or a GOS is necessary for such intendedusage may be labeled separately from the features. The server mayseparately hold, as an upper layer of a SWLD (e.g., a lane world), FVXLsof a signal or an intersection, etc. necessary for self-locationestimation, driving assist, or self-driving, etc.

The server may also add an attribute to VXLs in a WLD on a random accessbasis or on a predetermined unit basis. An attribute, for example,includes information indicating whether VXLs are necessary forself-location estimation, or information indicating whether VXLs areimportant as traffic information such as a signal, or an intersection,etc. An attribute may also include a correspondence between VXLs andfeatures (intersection, or road, etc.) in lane information (geographicdata files (GDF), etc.).

A method as described below may be used to update a WLD or a SWLD.

Update information indicating changes, etc. in a person, a roadwork, ora tree line (for trucks) is uploaded to the server as point groups ormeta data. The server updates a WLD on the basis of such uploadedinformation, and then updates a SWLD by use of the updated WLD.

The client, when detecting a mismatch between the three-dimensionalinformation such client has generated at the time of self-locationestimation and the three-dimensional information received from theserver, may send to the server the three-dimensional information suchclient has generated, together with an update notification. In such acase, the server updates the SWLD by use of the WLD. When the SWLD isnot to be updated, the server judges that the WLD itself is old.

In the above description, information that distinguishes whether anencoded stream is that of a WLD or a SWLD is added as header informationof the encoded stream. However, when there are many types of worlds suchas a mesh world and a lane world, information that distinguishes thesetypes of the worlds may be added to header information. Also, when thereare many SWLDs with different amounts of features, information thatdistinguishes the respective SWLDs may be added to header information.

In the above description, a SWLD is constituted by FVXLs, but a SWLD mayinclude VXLs that have not been judged as FVXLs. For example, a SWLD mayinclude an adjacent VXL used to calculate the feature of a FVXL. Thisenables the client to calculate the feature of a FVXL when receiving aSWLD, even in the case where feature information is not added to eachFVXL of the SWLD. In such a case, the SWLD may include information thatdistinguishes whether each VXL is a FVXL or a VXL.

As described above, three-dimensional data encoding device 400 extracts,from input three-dimensional data 411 (first three-dimensional data),extracted three-dimensional data 412 (second three-dimensional data)having an amount of a feature greater than or equal to a threshold, andencodes extracted three-dimensional data 412 to generate encodedthree-dimensional data 414 (first encoded three-dimensional data).

This three-dimensional data encoding device 400 generates encodedthree-dimensional data 414 that is obtained by encoding data having anamount of a feature greater than or equal to the threshold. This reducesthe amount of data compared to the case where input three-dimensionaldata 411 is encoded as it is. Three-dimensional data encoding device 400is thus capable of reducing the amount of data to be transmitted.

Three-dimensional data encoding device 400 further encodes inputthree-dimensional data 411 to generate encoded three-dimensional data413 (second encoded three-dimensional data).

This three-dimensional data encoding device 400 enables selectivetransmission of encoded three-dimensional data 413 and encodedthree-dimensional data 414, in accordance, for example, with theintended use, etc.

Also, extracted three-dimensional data 412 is encoded by a firstencoding method, and input three-dimensional data 411 is encoded by asecond encoding method different from the first encoding method.

This three-dimensional data encoding device 400 enables the use of anencoding method suitable for each of input three-dimensional data 411and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first encoding method than in thesecond encoding method.

This three-dimensional data encoding device 400 enables inter predictionto be more preferentially performed on extracted three-dimensional data412 in which adjacent data items are likely to have low correlation.

Also, the first encoding method and the second encoding method representthree-dimensional positions differently. For example, the secondencoding method represents three-dimensional positions by octree, andthe first encoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data encoding device 400 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Stated differently, such identifierindicates whether the encoded three-dimensional data is encodedthree-dimensional data 413 of a WLD or encoded three-dimensional data414 of a SWLD.

This enables the decoding device to readily judge whether the obtainedencoded three-dimensional data is encoded three-dimensional data 413 orencoded three-dimensional data 414.

Also, three-dimensional data encoding device 400 encodes extractedthree-dimensional data 412 in a manner that encoded three-dimensionaldata 414 has a smaller data amount than a data amount of encodedthree-dimensional data 413.

This three-dimensional data encoding device 400 enables encodedthree-dimensional data 414 to have a smaller data amount than the dataamount of encoded three-dimensional data 413.

Also, three-dimensional data encoding device 400 further extracts datacorresponding to an object having a predetermined attribute from inputthree-dimensional data 411 as extracted three-dimensional data 412. Theobject having a predetermined attribute is, for example, an objectnecessary for self-location estimation, driving assist, or self-driving,etc., or more specifically, a signal, an intersection, etc.

This three-dimensional data encoding device 400 is capable of generatingencoded three-dimensional data 414 that includes data required by thedecoding device.

Also, three-dimensional data encoding device 400 (server) further sends,to a client, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a status of the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Also, three-dimensional data encoding device 400 further sends, to aclient, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a request from the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the request from the client.

Also, three-dimensional data decoding device 500 according to thepresent embodiment decodes encoded three-dimensional data 413 or encodedthree-dimensional data 414 generated by three-dimensional data encodingdevice 400 described above.

Stated differently, three-dimensional data decoding device 500 decodes,by a first decoding method, encoded three-dimensional data 414 obtainedby encoding extracted three-dimensional data 412 having an amount of afeature greater than or equal to a threshold, extractedthree-dimensional data 412 having been extracted from inputthree-dimensional data 411. Three-dimensional data decoding device 500also decodes, by a second decoding method, encoded three-dimensionaldata 413 obtained by encoding input three-dimensional data 411, thesecond decoding method being different from the first decoding method.

This three-dimensional data decoding device 500 enables selectivereception of encoded three-dimensional data 414 obtained by encodingdata having an amount of a feature greater than or equal to thethreshold and encoded three-dimensional data 413, in accordance, forexample, with the intended use, etc. Three-dimensional data decodingdevice 500 is thus capable of reducing the amount of data to betransmitted. Such three-dimensional data decoding device 500 furtherenables the use of a decoding method suitable for each of inputthree-dimensional data 411 and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first decoding method than in thesecond decoding method.

This three-dimensional data decoding device 500 enables inter predictionto be more preferentially performed on the extracted three-dimensionaldata in which adjacent data items are likely to have low correlation.

Also, the first decoding method and the second decoding method representthree-dimensional positions differently. For example, the seconddecoding method represents three-dimensional positions by octree, andthe first decoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data decoding device 500 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Three-dimensional data decoding device 500refers to such identifier in identifying between encodedthree-dimensional data 413 and encoded three-dimensional data 414.

This three-dimensional data decoding device 500 is capable of readilyjudging whether the obtained encoded three-dimensional data is encodedthree-dimensional data 413 or encoded three-dimensional data 414.

Three-dimensional data decoding device 500 further notifies a server ofa status of the client (three-dimensional data decoding device 500).Three-dimensional data decoding device 500 receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the status of the client.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Three-dimensional data decoding device 500 further makes a request ofthe server for one of encoded three-dimensional data 418 and encodedthree-dimensional data 414, and receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the request.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the intended use.

Embodiment 3

The present embodiment will describe a method of transmitting/receivingthree-dimensional data between vehicles. For example, thethree-dimensional data is transmitted/received between the own vehicleand the nearby vehicle.

FIG. 24 is a block diagram of three-dimensional data creation device 620according to the present embodiment. Such three-dimensional datacreation device 620, which is included, for example, in the own vehicle,mergers first three-dimensional data 632 created by three-dimensionaldata creation device 620 with the received second three-dimensional data635, thereby creating third three-dimensional data 636 having a higherdensity.

Such three-dimensional data creation device 620 includesthree-dimensional data creator 621, request range determiner 622,searcher 623, receiver 624, decoder 625, and merger 626.

First, three-dimensional data creator 621 creates firstthree-dimensional data 632 by use of sensor information 631 detected bythe sensor included in the own vehicle. Next, request range determiner622 determines a request range, which is the range of athree-dimensional space, the data on which is insufficient in thecreated first three-dimensional data 632.

Next, searcher 623 searches for the nearby vehicle having thethree-dimensional data of the request range, and sends request rangeinformation 633 indicating the request range to nearby vehicle 601having been searched out (S623). Next, receiver 624 receives encodedthree-dimensional data 634, which is an encoded stream of the requestrange, from nearby vehicle 601 (S624). Note that searcher 623 mayindiscriminately send requests to all vehicles included in a specifiedrange to receive encoded three-dimensional data 634 from a vehicle thathas responded to the request. Searcher 623 may send a request not onlyto vehicles but also to an object such as a signal and a sign, andreceive encoded three-dimensional data 634 from the object.

Next, decoder 625 decodes the received encoded three-dimensional data634, thereby obtaining second three-dimensional data 635. Next, merger626 merges first three-dimensional data 632 with secondthree-dimensional data 635, thereby creating three-dimensional data 636having a higher density.

Next, the structure and operations of three-dimensional datatransmission device 640 according to the present embodiment will bedescribed. FIG. 25 is a block diagram of three-dimensional datatransmission device 640.

Three-dimensional data transmission device 640 is included, for example,in the above-described nearby vehicle. Three-dimensional datatransmission device 640 processes fifth three-dimensional data 652created by the nearby vehicle into sixth three-dimensional data 654requested by the own vehicle, encodes sixth three-dimensional data 654to generate encoded three-dimensional data 634, and sends encodedthree-dimensional data 634 to the own vehicle.

Three-dimensional data transmission device 640 includesthree-dimensional data creator 641, receiver 642, extractor 643, encoder644, and transmitter 645.

First, three-dimensional data creator 641 creates fifththree-dimensional data 652 by use of sensor information 651 detected bythe sensor included in the nearby vehicle. Next, receiver 642 receivesrequest range information 633 from the own vehicle.

Next, extractor 643 extracts from fifth three-dimensional data 652 thethree-dimensional data of the request range indicated by request rangeinformation 633, thereby processing fifth three-dimensional data 652into sixth three-dimensional data 654. Next, encoder 644 encodes sixththree-dimensional data 654 to generate encoded three-dimensional data643, which is an encoded stream. Then, transmitter 645 sends encodedthree-dimensional data 634 to the own vehicle.

Note that although an example case is described here in which the ownvehicle includes three-dimensional data creation device 620 and thenearby vehicle includes three-dimensional data transmission device 640,each of the vehicles may include the functionality of boththree-dimensional data creation device 620 and three-dimensional datatransmission device 640.

Embodiment 4

The present embodiment describes operations performed in abnormal caseswhen self-location estimation. is performed on the basis of athree-dimensional map.

A three-dimensional map is expected to find its expanded use inself-driving of a vehicle and autonomous movement, etc. of a mobileobject such as a robot and a flying object (e.g., a drone). Examplemeans for enabling such autonomous movement include a method in which amobile object travels in accordance with a three-dimensional map, whileestimating its self-location on the map (self-location estimation).

The self-location estimation is enabled by matching a three-dimensionalmap with three-dimensional information on the surrounding of the ownvehicle (hereinafter referred to as self-detected three-dimensionaldata) obtained by a sensor equipped in the own vehicle, such as arangefinder (e,g, a LiDAR) and a stereo camera to estimate the locationof the own vehicle on the three-dimensional map.

As in the case of an HD map suggested by HERE Technologies, for example,a three-dimensional map may include not only a three-dimensional pointcloud, but also two-dimensional map data such as information on theshapes of roads and intersections, or information that changes inreal-time such as information on a traffic jam and an accident. Athree-dimensional map includes a plurality of layers such as layers ofthree-dimensional data, two-dimensional data, and meta-data that changesin real-time, from among which the device can obtain or refer to onlynecessary data.

Point cloud data may be a SWLD as described above, or may include pointgroup data that is different from keypoints. The transmission/receptionof point cloud data is basically carried out in one or more randomaccess units.

A method described below is used as a method of matching athree-dimensional map with self-detected three-dimensional data. Forexample, the device compares the shapes of the point groups in eachother's point clouds, and determines that portions having a high degreeof similarity among keypoints correspond to the same position. When thethree-dimensional map is formed by a SWLD, the device also performsmatching by comparing the keypoints that form the SWLD withthree-dimensional keypoints extracted from the self-detectedthree-dimensional data.

Here, to enable highly accurate self-location estimation, the followingneeds to be satisfied: (A) the three-dimensional map and theself-detected three-dimensional data have been already obtained; and (B)their accuracies satisfy a predetermined requirement. However, one of(A) and (B) cannot be satisfied in abnormal cases such as ones describedbelow.

1. A three-dimensional map is unobtainable over communication.

2. A three-dimensional map is not present, or a three-dimensional maphaving been obtained is corrupt.

3. A sensor of the own vehicle has trouble, or the accuracy of thegenerated self-detected three-dimensional data is inadequate due to badweather.

The following describes operations to cope with such abnormal cases. Thefollowing description illustrates an example case of a vehicle, but themethod described below is applicable to mobile objects on the whole thatare capable of autonomous movement, such as a robot and a drone.

The following describes the structure of the three-dimensionalinformation processing device and its operation according to the presentembodiment capable of coping with abnormal cases regarding athree-dimensional map or self-detected three-dimensional data. FIG. 26is a block diagram of an example structure of three-dimensionalinformation processing device 700 according to the present embodiment.

Three-dimensional information processing device 700 is equipped, forexample, in a mobile object such as a car. As shown in FIG. 26,three-dimensional information processing device 700 includesthree-dimensional map obtainer 701, self-detected data obtainer 702,abnormal case judgment unit 703, coping operation determiner 704, andoperation controller 705.

Note that three-dimensional information processing device 700 mayinclude a non-illustrated two-dimensional or one-dimensional sensor thatdetects a structural object or a mobile object around the own vehicle,such as a camera capable of obtaining two-dimensional images and asensor for one-dimensional data utilizing ultrasonic or laser.Three-dimensional information processing device 700 may also include anon-illustrated communication unit that obtains a three-dimensional mapover a mobile communication network, such as 4G and 5G, or viainter-vehicle communication or road-to-vehicle communication.

Three-dimensional map obtainer 701 obtains three-dimensional map 711 ofthe surroundings of the traveling route. For example, three-dimensionalmap obtainer 701 obtains three-dimensional map 711 over a mobilecommunication network, or via inter-vehicle communication orroad-to-vehicle communication.

Next, self-detected data obtainer 702 obtains self-detectedthree-dimensional data 712 on the basis of sensor information. Forexample, self-detected data obtainer 702 generates self-detectedthree-dimensional data 712 on the basis of the sensor informationobtained by a sensor equipped in the own vehicle.

Next, abnormal case judgment unit 703 conducts a predetermined check ofat least one of obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 to detect an abnormal case. Stateddifferently, abnormal case judgment unit 703 judges whether at least oneof obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 is abnormal.

When the abnormal case is detected, coping operation determiner 704determines a coping operation to cope with such abnormal case. Next,operation controller 705 controls the operation of each of theprocessing units necessary to perform the coping operation.

Meanwhile, when no abnormal case is detected, three-dimensionalinformation processing device 700 terminates the process.

Also, three-dimensional information processing device 700 estimates thelocation of the vehicle equipped with three-dimensional informationprocessing device 700, using three-dimensional map 711 and self-detectedthree-dimensional data 712. Next, three-dimensional informationprocessing device 700 performs the automatic operation of the vehicle byuse of the estimated location of the vehicle.

As described above, three-dimensional information processing device 700obtains, via a communication channel, map data (three-dimensional map711) that includes first three-dimensional position information. Thefirst three-dimensional position information includes, for example, aplurality of random access units, each of which is an assembly of atleast one subspace and is individually decodable, the at least onesubspace having three-dimensional coordinates information and serving asa unit in which each of the plurality of random access units is encoded.The first three-dimensional position information is, for example, data(SWLD) obtained by encoding keypoints, each of which has an amount of athree-dimensional feature greater than or equal to a predeterminedthreshold.

Three-dimensional information processing device 700 also generatessecond three-dimensional position information (self-detectedthree-dimensional data 712) from information detected by a sensor.Three-dimensional information processing device 700 then judges whetherone of the first three-dimensional position information and the secondthree-dimensional position information is abnormal by performing, on oneof the first three-dimensional position information and the secondthree-dimensional position information, a process of judging whether anabnormality is present.

Three-dimensional information processing device 700 determines a copingoperation to cope with the abnormality when one of the firstthree-dimensional position information and the second three-dimensionalposition information is judged to be abnormal. Three-dimensionalinformation processing device 700 then executes a control that isrequired to perform the coping operation.

This structure enables three-dimensional information processing device700 to detect an abnormality regarding one of the firstthree-dimensional position information and the second three-dimensionalposition information, and to perform a coping operation therefor.

Embodiment 5

The present embodiment describes a method, etc. of transmittingthree-dimensional data to a following vehicle.

FIG. 27 is a block diagram of an exemplary structure ofthree-dimensional data creation device 810 according to the presentembodiment. Such three-dimensional data creation device 810 is equipped,for example, in a vehicle. Three-dimensional data creation device 810transmits and receives three-dimensional data to and from an externalcloud-based traffic monitoring system, a preceding vehicle, or afollowing vehicle, and creates and stores three-dimensional data.

Three-dimensional data creation device 810 includes data receiver 811,communication unit 812, reception controller 813, format converter 814,a plurality of sensors 815, three-dimensional data creator 816,three-dimensional data synthesizer 817, three-dimensional data storage818, communication unit 819, transmission controller 820, formatconverter 821, and data transmitter 822.

Data receiver 811 receives three-dimensional data 831 from a cloud-basedtraffic monitoring system or a preceding vehicle. Three-dimensional data831 includes, for example, information on a region undetectable bysensors 815 of the own vehicle, such as a point cloud, visible lightvideo, depth information, sensor position information, and speedinformation.

Communication unit 812 communicates with the cloud-based trafficmonitoring system or the preceding vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the preceding vehicle.

Reception controller 813 exchanges information, such as information onsupported formats, with a communications partner via communication unit812 to establish communication with the communications partner.

Format converter 814 applies format conversion, etc. onthree-dimensional data 831 received by data receiver 811 to generatethree-dimensional data 832. Format converter 814 also decompresses ordecodes three-dimensional data 831 when three-dimensional data 831 iscompressed or encoded.

A plurality of sensors 815 are a group of sensors, such as visible lightcameras and infrared cameras, that obtain information on the outside ofthe vehicle and generate sensor information 833. Sensor information 833is, for example, three-dimensional data such as a point cloud (pointgroup data), when sensors 815 are laser sensors such as LiDARs. Notethat a single sensor may serve as a plurality of sensors 815.

Three-dimensional data creator 816 generates three-dimensional data 834from sensor information 833. Three-dimensional data 834 includes, forexample, information such as a point cloud, visible light video, depthinformation, sensor position information, and speed information.

Three-dimensional data synthesizer 817 synthesizes three-dimensionaldata 834 created on the basis of sensor information 833 of the ownvehicle with three-dimensional data 832 created by the cloud-basedtraffic monitoring system or the preceding vehicle, etc., therebyforming three-dimensional data 835 of a space that includes the spaceahead of the preceding vehicle undetectable by sensors 815 of the ownvehicle.

Three-dimensional data storage 818 stores generated three-dimensionaldata 835, etc.

Communication unit 819 communicates with the cloud-based trafficmonitoring system or the following vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the following vehicle.

Transmission controller 820 exchanges information such as information onsupported formats with a communications partner via communication unit819 to establish communication with the communications partner.Transmission controller 820 also determines a transmission region, whichis a space of the three-dimensional data to be transmitted, on the basisof three-dimensional data formation information on three-dimensionaldata 832 generated by three-dimensional data synthesizer 817 and thedata transmission request from the communications partner.

More specifically, transmission controller 820 determines a transmissionregion that includes the space ahead of the own vehicle undetectable bya sensor of the following vehicle, in response to the data transmissionrequest from the cloud-based traffic monitoring system or the followingvehicle. Transmission controller 820 judges, for example, whether aspace is transmittable or whether the already transmitted space includesan update, on the basis of the three-dimensional data formationinformation to determine a transmission region. For example,transmission controller 820 determines, as a transmission region, aregion that is: a region specified by the data transmission request; anda region, corresponding three-dimensional data 835 of which is present.Transmission controller 820 then notifies format converter 821 of theformat supported by the communications partner and the transmissionregion.

Of three-dimensional data 835 stored in three-dimensional data storage818, format converter 821 converts three-dimensional data 836 of thetransmission region into the format supported by the receiver end togenerate three-dimensional data 837. Note that format converter 821 maycompress or encode three-dimensional data 837 to reduce the data amount.

Data transmitter 822 transmits three-dimensional data 837 to thecloud-based traffic monitoring system or the following vehicle. Suchthree-dimensional data 837 includes, for example, information on a blindspot, which is a region hidden from view of the following vehicle, suchas a point cloud ahead of the own vehicle, visible light video, depthinformation, and sensor position information.

Note that an example has been described in which format converter 814and format converter 821 perform format conversion, etc., but formatconversion may not be performed.

With the above structure, three-dimensional data creation device 810obtains, from an external device, three-dimensional data 831 of a regionundetectable by sensors 815 of the own vehicle, and synthesizesthree-dimensional data 831 with three-dimensional data 834 that is basedon sensor information 833 detected by sensors 815 of the own vehicle,thereby generating three-dimensional data 835. Three-dimensional datacreation device 810 is thus capable of generating three-dimensional dataof a range undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 is also capable oftransmitting, to the cloud-based traffic monitoring system or thefollowing vehicle, etc., three-dimensional data of a space that includesthe space ahead of the own vehicle undetectable by a sensor of thefollowing vehicle, in response to the data transmission request from thecloud-based traffic monitoring system or the following vehicle.

Embodiment 6

In embodiment 5, an example is described in which a client device of avehicle or the like transmits three-dimensional data to another vehicleor a server such as a cloud-based traffic monitoring system. In thepresent embodiment, a client device transmits sensor informationobtained through a sensor to a server or a client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 28 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LiDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information.about client device 902. Server 901 may also send the transmissionrequest for the sensor information, when wanting to (i) update thethree-dimensional map, check road conditions during snowfall, adisaster, or the like, or (iii) check traffic congestion conditions,accident/incident conditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 29 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g. transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 815 are a group of sensors, such as LiDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LiDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LiDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LiDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 30 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LiDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g. transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when received sensor information 1037is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LiDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 31is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 32 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 33 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 34 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

Hereinafter, variations of the present embodiment will be described.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfront client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LiDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 35 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 9020. Client device902A receives the high-precision three-dimensional map from clientdevice 9020 in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located. nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 36 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmitsobtained sensor information 1033 to server 901 or another mobile object.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1034 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another mobileobject 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using received sensor information1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses received sensor information 1037, andcreates three-dimensional data 1134 using sensor information 1132 thathas been decoded or decompressed. This enables server 901 to reduce theamount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

Embodiment 7

In the present embodiment, three-dimensional data encoding and decodingmethods using an inter prediction process will be described.

FIG. 37 is a block diagram of three-dimensional data encoding device1300 according to the present embodiment. This three-dimensional dataencoding device 1300 generates an encoded bitstream (hereinafter, alsosimply referred to as bitstream) that is an encoded signal, by encodingthree-dimensional data. As illustrated in FIG. 37, three-dimensionaldata encoding device 1300 includes divider 1301, subtractor 1302,transformer 1303, quantizer 1304, inverse quantizer 1305, inversetransformer 1306, adder 1307, reference volume memory 1308, intrapredictor 1309, reference space memory 1310, inter predictor 1311,prediction controller 1312, and entropy encoder 1313.

Divider 1301 divides a plurality of volumes (VLMs) that are encodingunits of each space (SPC) included in the three-dimensional data.Divider 1301 makes an octree representation (make into an octree) ofvoxels in each volume. Note that divider 1301 may make the spaces intoan octree representation with the spaces having the same size as thevolumes. Divider 1301 may also append information (depth information,etc.) necessary for making the octree representation to a header and thelike of a bitstream.

Subtractor 1302 calculates a difference between a volume (encodingtarget volume) outputted by divider 1301 and a predicted volumegenerated through intra prediction or inter prediction, which will bedescribed later, and outputs the calculated difference to transformer1303 as a prediction residual. FIG. 38 is a diagram showing an examplecalculation of the prediction residual. Note that bit sequences of theencoding target volume and the predicted volume shown here are, forexample, position information indicating positions of three-dimensionalpoints included in the volumes.

Hereinafter, a scan order of an octree representation and voxels will bedescribed. A volume is encoded after being converted into an octreestructure (made into an octree). The octree structure includes nodes andleaves. Each node has eight nodes or leaves, and each leaf has voxel(VXL) information. FIG. 39 is a diagram showing an example structure ofa volume including voxels. FIG. 40 is a diagram showing an example ofthe volume shown in FIG. 39 having been converted into the octreestructure. Among the leaves shown in FIG. 40, leaves 1, 2, and 3respectively represent VXL 1, VXL 2, and VXL 3, and represent VXLsincluding a point group (hereinafter, active VXLs).

An octree is represented by, for example, binary sequences of 1s and 0s.For example, when giving the nodes or the active VXLs a value of 1 andeverything else a value of 0, each node and leaf is assigned with thebinary sequence shown in FIG. 40. Thus, this binary sequence is scannedin accordance with a breadth-first or a depth-first scan order. Forexample, when scanning breadth-first, the binary sequence shown in A ofFIG. 41 is obtained. When scanning depth-first, the binary sequenceshown in B of FIG. 41 is obtained. The binary sequences obtained throughthis scanning are encoded through entropy encoding, which reduces anamount of information.

Depth information in the octree representation will be described next.Depth in the octree representation is used in order to control up to howfine a granularity point cloud information included in a volume isstored. Upon setting a great depth, it is possible to reproduce thepoint cloud information to a more precise level, but an amount of datafor representing the nodes and leaves increases. Upon setting a smalldepth, however, the amount of data decreases, but some information thatthe point cloud information originally held is lost, since pieces ofpoint cloud information including different positions and differentcolors are now considered as pieces of point cloud information includingthe same position and the same color.

For example, FIG. 42 is a diagram showing an example in which the octreewith a depth of 2 shown in FIG. 40 is represented with a depth of 1. Theoctree shown in FIG. 42 has a lower amount of data than the octree shownin FIG. 40. In other words, the binarized octree shown in FIG. 42 has alower bit count than the octree shown in FIG. 40. Leaf 1 and leaf 2shown in FIG. 40 are represented by leaf 1 shown in FIG. 41, In otherwords, the information on leaf 1 and leaf 2 being in different positionsis lost.

FIG. 43 is a diagram showing a volume corresponding to the octree shownin FIG. 42. VXL 1 and VXL 2 shown in FIG. 39 correspond to VXL 12 shownin FIG. 43. In this case, three-dimensional data encoding device 1300generates color information of VXL 12 shown in FIG. 43 using colorinformation of VXL 1 and VXL 2 shown in FIG. 39. For example,three-dimensional data encoding device 1300 calculates an average value,a median, a weighted average value, or the like of the color informationof VXL 1 and VXL 2 as the color information of VXL 12. In this manner,three-dimensional data encoding device 1300 may control a reduction ofthe amount of data by changing the depth of the octree.

Three-dimensional data encoding device 1300 may set the depthinformation of the octree to units of worlds, units of spaces, or unitsof volumes. In this case, three-dimensional data encoding device 1300may append the depth information to header information of the world,header information of the space, or header information of the volume. Inall worlds, spaces, and volumes associated with different times, thesame value may be used as the depth information. In this case,three-dimensional data encoding device 1300 may append the depthinformation to header information managing the worlds associated withall times.

When the color information is included in the voxels, transformer 1303applies frequency transformation, e.g. orthogonal transformation, to aprediction residual of the color information of the voxels in thevolume. For example, transformer 1303 creates a one-dimensional array byscanning the prediction residual in a certain scan order. Subsequently,transformer 1303 transforms the one-dimensional array to a frequencydomain by applying one-dimensional orthogonal transformation to thecreated one-dimensional array. With this, when a value of the predictionresidual in the volume is similar, a value of a low-frequency componentincreases and a value of a high-frequency component decreases. As such,it is possible to more efficiently reduce an code amount in quantizer1304.

Transformer 1303 does not need to use orthogonal transformation in onedimension, but may also use orthogonal transformation in two or moredimensions. For example, transformer 1303 maps the prediction residualto a two-dimensional array in a certain scan order, and appliestwo-dimensional orthogonal transformation to the obtainedtwo-dimensional array. Transformer 1303 may select an orthogonaltransformation method to be used from a plurality of orthogonaltransformation methods. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, information indicating whichorthogonal transformation method is used. Transformer 1303 may select anorthogonal transformation method to be used from a plurality oforthogonal transformation methods in different dimensions. In this case,three-dimensional data encoding device 1300 appends, to the bitstream,in how many dimensions the orthogonal transformation method is used.

For example, transformer 1303 matches the scan order of the predictionresidual to a scan order (breadth-first, depth-first, or the like) inthe octree in the volume. This makes it possible to reduce overhead,since information indicating the scan order of the prediction residualdoes not need to be appended to the bitstream. Transformer 1303 mayapply a scan order different from the scan order of the octree. In thiscase, three-dimensional data encoding device 1300 appends, to thebitstream, information indicating the scan order of the predictionresidual. This enables three-dimensional data encoding device 1300 toefficiently encode the prediction residual. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag,etc.) indicating whether to apply the scan order of the octree, and mayalso append, to the bitstream, information indicating the scan order ofthe prediction residual when the scan order of the octree is notapplied.

Transformer 1303 does not only transform the prediction residual of thecolor information, and may also transform other attribute informationincluded in the voxels. For example, transformer 1303 may transform andencode information, such as reflectance information, obtained whenobtaining a point cloud through LiDAR and the like.

Transformer 1303 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of transformer 1303.

Quantizer 1304 generates a quantized coefficient by performingquantization using a quantization control parameter on a frequencycomponent of the prediction residual generated by transformer 1303. Withthis, the amount of information is further reduced. The generatedquantized coefficient is outputted to entropy encoder 1313. Quantizer1304 may control the quantization control parameter in units of worlds,units of spaces, or units of volumes. In this case, three-dimensionaldata encoding device 1300 appends the quantization control parameter toeach header information and the like. Quantizer 1304 may performquantization control by changing a weight per frequency component of theprediction residual. For example, quantizer 1304 may precisely quantizea low-frequency component and roughly quantize a high-frequencycomponent. In this case, three-dimensional data encoding device 1300 mayappend, to a header, a parameter expressing a weight of each frequencycomponent.

Quantizer 1304 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of quantizer 1304.

Inverse quantizer 1305 generates an inverse quantized coefficient of theprediction residual by performing inverse quantization on the quantizedcoefficient generated by quantizer 1304 using the quantization controlparameter, and outputs the generated inverse quantized coefficient toinverse transformer 1306.

Inverse transformer 1306 generates an inverse transformation-appliedprediction residual by applying inverse transformation on the inversequantized coefficient generated by inverse quantizer 1305. This inversetransformation-applied prediction residual does not need to completelycoincide with the prediction residual outputted by transformer 1303,since the inverse transformation-applied prediction residual is aprediction residual that is generated after the quantization.

Adder 1307 adds, to generate a reconstructed volume, (i) the inversetransformation-applied prediction residual generated by inversetransformer 1306 to (ii) a predicted volume that is generated throughintra prediction or intra prediction, which will be described later, andis used to generate a pre-quantized prediction residual. Thisreconstructed volume is stored in reference volume memory 1308 orreference space memory 1310.

Intra predictor 1309 generates a predicted volume of an encoding targetvolume using attribute information of a neighboring volume stored inreference volume memory 1308. The attribute information includes colorinformation or a reflectance of the voxels. Intra predictor 1309generates a predicted value of color information or a reflectance of theencoding target volume.

FIG. 44 is a diagram for describing an operation of intra predictor1309. For example, intra predictor 1309 generates the predicted volumeof the encoding target volume (volume idx=3) shown in FIG. 44, using aneighboring volume (volume idx=0). Volume idx here is identifierinformation that is appended to a volume in a space, and a differentvalue is assigned to each volume. An order of assigning volume idx maybe the same as an encoding order, and may also be different from theencoding order. For example, intra predictor 1309 uses an average valueof color information of voxels included in volume idx=0, which is aneighboring volume, as the predicted value of the color information ofthe encoding target volume shown in FIG. 44. In this case, a predictionresidual is generated by deducting the predicted value of the colorinformation from the color information of each voxel included in theencoding target volume. The following processes are performed bytransformer 1303 and subsequent processors with respect to thisprediction residual. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, neighboring volume informationand prediction mode information. The neighboring volume information hereis information indicating a neighboring volume used in the prediction,and indicates, for example, volume idx of the neighboring volume used inthe prediction. The prediction mode information here indicates a modeused to generate the predicted volume. The mode is, for example, anaverage value mode in which the predicted value is generated using anaverage value of the voxels in the neighboring volume, or a median modein which the predicted value is generated using the median of the voxelsin the neighboring volume.

Intra predictor 1309 may generate the predicted volume using a pluralityof neighboring volumes. For example, in the structure shown in FIG. 44,intra predictor 1309 generates predicted volume 0 using a volume withvolume idx=0, and generates predicted volume 1 using a volume withvolume idx=1. Intra predictor 1309 then generates an average ofpredicted volume 0 and predicted volume 1 as a final predicted volume.In this case, three-dimensional data encoding device 1300 may append, tothe bitstream, a plurality of volumes idx of a plurality of volumes usedto generate the predicted volume.

FIG. 45 is a diagram schematically showing the inter prediction processaccording to the present embodiment. Inter predictor 1311 encodes (interpredicts) a space (SPC) associated with certain time T_Cur using anencoded space associated with different time T_LX. In this case, interpredictor 1311 performs an encoding process by applying a rotation andtranslation process to the encoded space associated with different timeT_LX.

Three-dimensional data encoding device 1300 appends, to the bitstream,RT information relating to a rotation and translation process suited tothe space associated with different time T_LX. Different time T_LX is,for example, time T_LO before certain time T_Cur. At this point,three-dimensional data encoding device 1300 may append, to thebitstream, RT information RT_L0 relating to a rotation and translationprocess suited to a space associated with time T_L0.

Alternatively, different time T_LX. is, for example, time T_L1 aftercertain time T_Cur. At this point, three-dimensional data encodingdevice 1300 may append, to the bitstream, RT information RT_L1 relatingto a rotation and translation process suited to a space associated withtime T_L1.

Alternatively, inter predictor 1311 encodes (bidirectional prediction)with reference to the spaces associated with time T_L0 and time T_L1that differ from each other. In this case, three-dimensional dataencoding device 1300 may append, to the bitstream, both RT informationRT_L0 and RT information RT_L1 relating to the rotation and translationprocess suited to the spaces thereof.

Note that T_L0 has been described as being before T_Cur and T_L1 asbeing after T_Cur, but are not necessarily limited thereto. For example,T_L0 and T_L1 may both be before T_Cur. T_L0 and T_L1 may also both beafter T_Cur.

Three-dimensional data encoding device 1300 may append, to thebitstream, RT information relating to a rotation and translation processsuited to spaces associated with different times, when encoding withreference to each of the spaces. For example, three-dimensional dataencoding device 1300 manages a plurality of encoded spaces to bereferred to, using two reference lists (list L0 and list L1). When afirst reference space in list L0 is L0R0, a second reference space inlist L0 is L0R1, a first reference space in list L1 is L1R0, and asecond reference space in list L1 is L1R1, three-dimensional dataencoding device 1300 appends, to the bitstream, RT information RT_L0R0of L0R0, RT information RT_L0R1 of L0R1, RT information RT_L1R0 of L1R0,and RT information RT_L1R1 of L1R1. For example, three-dimensional dataencoding device 1300 appends these pieces of RT information to a headerand the like of the bitstream.

Three-dimensional data encoding device 1300 determines whether to applyrotation and translation per reference space, when encoding withreference to reference spaces associated with different times. In thiscase, three-dimensional data encoding device 1300 may append, to headerinformation and the like of the bitstream, information (RT flag, etc.)indicating whether rotation and translation are applied per referencespace. For example, three-dimensional data encoding device 1300calculates the RT information and an Iterative Closest Point (ICP) errorvalue, using an ICP algorithm per reference space to be referred to fromthe encoding target space. Three-dimensional data encoding device 1300determines that rotation and translation do not need to be performed andsets the RT flag to OFF, when the ICP error value is lower than or equalto a predetermined fixed value. In contrast, three-dimensional dataencoding device 1300 sets the RT flag to ON and appends the RTinformation to the bitstream, when the ICP error value exceeds the abovefixed value.

FIG. 46 is a diagram showing an example syntax to be appended to aheader of the RT information and the RT flag. Note that a bit countassigned to each syntax may be decided based on a range of this syntax.For example, when eight reference spaces are included in reference listL0, 3 bits may be assigned to MaxRefSpc_10. The bit count to be assignedmay be variable in accordance with a value each syntax can be, and mayalso be fixed regardless of the value each syntax can be. When the bitcount to be assigned is fixed, three-dimensional data encoding device1300 may append this fixed bit count to other header information.

MaxRefSpc_10 shown in FIG. 46 indicates a number of reference spacesincluded in reference list L0. RT_flag_10[i] is an RT flag of referencespace i in reference list L0. When RT_flag_10[i] is 1, rotation andtranslation are applied to reference space i. When RT_flag_10[i] is 0,rotation and translation are not applied to reference space i.

R_10[i] and T_10[i] are RT information of reference space i in referencelist L0. R_10[i] is rotation information of reference space i inreference list L0. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_10[i] is translation information of reference space i inreference list L0. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

MaxRefSpc_11 indicates a number of reference spaces included inreference list L1. RT_flag_11[i] is an RT flag of reference space i inreference list L1. When RT_flag_11[i] is 1, rotation and translation areapplied to reference space i. When RT_flag_11[i] is 0, rotation andtranslation are not applied to reference space i.

R_11[i] and T_11[i] are RT information of reference space i in referencelist L1. R_11[i] is rotation information of reference space i inreference list L1. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_11[i] is translation information of reference space i inreference list L1. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

Inter predictor 1311 generates the predicted volume of the encodingtarget volume using information on an encoded reference space stored inreference space memory 1310. As stated above, before generating thepredicted volume of the encoding target volume, inter predictor 1311calculates RT information at an encoding target space and a referencespace using an ICP algorithm, in order to approach an overall positionalrelationship between the encoding target space and the reference space.Inter predictor 1311 then obtains reference space B by applying arotation and translation process to the reference space using thecalculated RT information. Subsequently, inter predictor 1311 generatesthe predicted volume of the encoding target volume in the encodingtarget space using information in reference space B. Three-dimensionaldata encoding device 1300 appends, to header information and the like ofthe encoding target space, the RT information used to obtain referencespace B.

In this manner, inter predictor 1311 is capable of improving precisionof the predicted volume by generating the predicted volume using theinformation of the reference space, after approaching the overallpositional relationship between the encoding target space and thereference space, by applying a rotation and translation process to thereference space. It is possible to reduce the code amount since it ispossible to limit the prediction residual. Note that an example has beendescribed in which ICP is performed using the encoding target space andthe reference space, but is not necessarily limited thereto. Forexample, inter predictor 1311 may calculate the RT information byperforming ICP using at least one of (i) an encoding target space inwhich a voxel or point cloud count is pruned, or (ii) a reference spacein which a voxel or point cloud count is pruned, in order to reduce theprocessing amount.

When the ICP error value obtained as a result of the ICP is smaller thana predetermined first threshold, i.e., when for example the positionalrelationship between the encoding target space and the reference spaceis similar, inter predictor 1311 determines that a rotation andtranslation process is not necessary, and the rotation and translationprocess does not need to be performed. In this case, three-dimensionaldata encoding device 1300 may control the overhead by not appending theRT information to the bitstream.

When the ICP error value is greater than a predetermined secondthreshold, inter predictor 1311 determines that a shape change betweenthe spaces is large, and intra prediction may be applied on all volumesof the encoding target space. Hereinafter, spaces to which intraprediction is applied will be referred to as intra spaces. The secondthreshold is greater than the above first threshold. The presentembodiment is not limited to ICP, and any type of method may be used aslong as the method calculates the RT information using two voxel sets ortwo point cloud sets.

When attribute information, e.g. shape or color information, is includedin the three-dimensional data, inter predictor 1311 searches, forexample, a volume whose attribute information, e.g, shape or colorinformation, is the most similar to the encoding target volume in thereference space, as the predicted volume of the encoding target volumein the encoding target space. This reference space is, for example, areference space on which the above rotation and translation process hasbeen performed. Inter predictor 1311 generates the predicted volumeusing the volume (reference volume) obtained through the search. FIG. 47is a diagram for describing a generating operation of the predictedvolume. When encoding the encoding target volume (volume idx=0) shown inFIG. 47 using inter prediction, inter predictor 1311 searches a volumewith a smallest prediction residual, which is the difference between theencoding target volume and the reference volume, while sequentiallyscanning the reference volume in the reference space. Inter predictor1311 selects the volume with the smallest prediction residual as thepredicted volume. The prediction residuals of the encoding target volumeand the predicted volume are encoded through the processes performed bytransformer 1303 and subsequent processors. The prediction residual hereis a difference between the attribute information of the encoding targetvolume and the attribute information of the predicted volume.Three-dimensional data encoding device 1300 appends, to the header andthe like of the bitstream, volume idx of the reference volume in thereference space, as the predicted volume.

In the example shown in FIG. 47, the reference volume with volume idx=4of reference space L0R0 is selected as the predicted volume of theencoding target volume. The prediction residuals of the encoding targetvolume and the reference volume, and reference volume idx=4 are thenencoded and appended to the bitstream.

Note that an example has been described in which the predicted volume ofthe attribute information is generated, but the same process may beapplied to the predicted volume of the position information.

Prediction controller 1312 controls whether to encode the encodingtarget volume using intra prediction or inter prediction. A modeincluding intra prediction and inter prediction is referred to here as aprediction mode. For example, prediction controller 1312 calculates theprediction residual when the encoding target volume is predicted usingintra prediction and the prediction residual when the encoding targetvolume is predicted using inter prediction as evaluation values, andselects the prediction mode whose evaluation value is smaller. Note thatprediction controller 1312 may calculate an actual code amount byapplying orthogonal transformation, quantization, and entropy encodingto the prediction residual of the intra prediction and the predictionresidual of the inter prediction, and select a prediction mode using thecalculated code amount as the evaluation value. Overhead information(reference volume idx information, etc.) aside from the predictionresidual may be added to the evaluation value. Prediction controller1312 may continuously select intra prediction when it has been decidedin advance to encode the encoding target space using intra space.

Entropy encoder 1313 generates an encoded signal (encoded bitstream) byvariable-length encoding the quantized coefficient, which is an inputfrom quantizer 1304. To be specific, entropy encoder 1313, for example,binarizes the quantized coefficient and arithmetically encodes theobtained binary signal.

A three-dimensional data decoding device that decodes the encoded signalgenerated by three-dimensional data encoding device 1300 will bedescribed next. FIG. 48 is a block diagram of three-dimensional datadecoding device 1400 according to the present embodiment. Thisthree-dimensional data decoding device 1400 includes entropy decoder1401, inverse quantizer 1402, inverse transformer 1403, adder 1404,reference volume memory 1405, intra predictor 1406, reference spacememory 1407, inter predictor 1408, and prediction controller 1409.

Entropy decoder 1401 variable-length decodes the encoded signal (encodedbitstream). For example, entropy decoder 1401 generates a binary signalby arithmetically decoding the encoded signal, and generates a quantizedcoefficient using the generated binary signal.

Inverse quantizer 1402 generates an inverse quantized coefficient byinverse quantizing the quantized coefficient inputted from entropydecoder 1401, using a quantization parameter appended to the bitstreamand the like.

Inverse transformer 1403 generates a prediction residual by inversetransforming the inverse quantized coefficient inputted from inversequantizer 1402. For example, inverse transformer 1403 generates theprediction residual by inverse orthogonally transforming the inversequantized coefficient, based on information appended to the bitstream.

Adder 1404 adds, to generate a reconstructed volume, (i) the predictionresidual generated by inverse transformer 1403 to (ii) a predictedvolume generated through intra prediction or intra prediction. Thisreconstructed volume is outputted as decoded three-dimensional data andis stored in reference volume memory 1405 or reference space memory1407.

Intra predictor 1406 generates a predicted volume through intraprediction using a reference volume in reference volume memory 1405 andinformation appended to the bitstream. To be specific, intra predictor1406 obtains neighboring volume information (e.g. volume idx) appendedto the bitstream and prediction mode information, and generates thepredicted volume through a mode indicated by the prediction modeinformation, using a neighboring volume indicated in the neighboringvolume information. Note that the specifics of these processes are thesame as the above-mentioned processes performed by intra predictor 1309,except for which information appended to the bitstream is used.

Inter predictor 1408 generates a predicted volume through interprediction using a reference space in reference space memory 1407 andinformation appended to the bitstream. To be specific, inter predictor1408 applies a rotation and translation process to the reference spaceusing the RT information per reference space appended to the bitstream,and generates the predicted volume using the rotated and translatedreference space. Note that when an RT flag is present in the bitstreamper reference space, inter predictor 1408 applies a rotation andtranslation process to the reference space in accordance with the RTflag. Note that the specifics of these processes are the same as theabove-mentioned processes performed by inter predictor 1311, except forwhich information appended to the bitstream is used.

Prediction controller 1409 controls whether to decode a decoding targetvolume using intra prediction or inter prediction. For example,prediction controller 1409 selects intra prediction or inter predictionin accordance with information that is appended to the bitstream andindicates the prediction mode to be used. Note that predictioncontroller 1409 may continuously select intra prediction when it hasbeen decided in advance to decode the decoding target space using intraspace.

Hereinafter, variations of the present embodiment will be described. Inthe present embodiment, an example has been described in which rotationand translation is applied in units of spaces, but rotation andtranslation may also be applied in smaller units. For example,three-dimensional data encoding device 1300 may divide a space intosubspaces, and apply rotation and translation in units of subspaces. Inthis case, three-dimensional data encoding device 1300 generates RTinformation per subspace, and appends the generated RT information to aheader and the like of the bitstream. Three-dimensional data encodingdevice 1300 may apply rotation and translation in units of volumes,which is an encoding unit. In this case, three-dimensional data encodingdevice 1300 generates RT information in units of encoded volumes, andappends the generated RT information to a header and the like of thebitstream. The above may also be combined. In other words,three-dimensional data encoding device 1300 may apply rotation andtranslation in large units and subsequently apply rotation andtranslation in small units. For example, three-dimensional data encodingdevice 1300 may apply rotation and translation in units of spaces, andmay also apply different rotations and translations to each of aplurality of volumes included in the obtained spaces.

In the present embodiment, an example has been described in whichrotation and translation is applied to the reference space, but is notnecessarily limited thereto. For example, three-dimensional dataencoding device 1300 may apply a scaling process and change a size ofthe three-dimensional data. Three-dimensional data encoding device 1300may also apply one or two of the rotation, translation, and scaling,When applying the processes in multiple stages and different units asstated above, a type of the processes applied in each unit may differ.For example, rotation and translation may be applied in units of spaces,and translation may be applied in units of volumes.

Note that these variations are also applicable to three-dimensional datadecoding device 1400.

As stated above, three-dimensional data encoding device 1300 accordingto the present embodiment performs the following processes. FIG. 48 is aflowchart of the inter prediction process performed by three-dimensionaldata encoding device 1300.

Three-dimensional data encoding device 1300 generates predicted positioninformation (e.g. predicted volume) using position information onthree-dimensional points included in three-dimensional reference data(e.g. reference space) associated with a time different from a timeassociated with current three-dimensional data (e.g. encoding targetspace) (S1301). To be specific, three-dimensional data encoding device1300 generates the predicted position information by applying a rotationand translation process to the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data encoding device 1300 may perform arotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Forexample, three-dimensional data encoding device 1300 searches a volumeamong a plurality of volumes included in the rotated and translatedreference space, whose position information differs the least from theposition information of the encoding target volume included in theencoding target space. Note that three-dimensional data encoding device1300 may perform the rotation and translation process, and thegenerating of the predicted position information in the same unit.

Three-dimensional data encoding device 1300 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41, the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

As illustrated in FIG. 46, three-dimensional data encoding device 1300encodes an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data. In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the RT flag. Three-dimensional dataencoding device 1300 encodes RT information that indicates contents ofthe rotation and translation process. In other words, three-dimensionaldata encoding device 1300 generates the encoded signal (encodedbitstream) including the RT information. Note that three-dimensionaldata encoding device 1300 may encode the RT information when the RT flagindicates to apply the rotation and translation process, and does notneed to encode the RT information when the RT flag indicates not toapply the rotation and translation process.

The three-dimensional data includes, for example, the positioninformation on the three-dimensional points and the attributeinformation (color information, etc.) of each three-dimensional point.Three-dimensional data encoding device 1300 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1302).

Three-dimensional data encoding device 1300 next encodes the positioninformation on the three-dimensional points included in the currentthree-dimensional data, using the predicted position information. Forexample, as illustrated in FIG. 38, three-dimensional data encodingdevice 1300 calculates differential position information, thedifferential position information being a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data(S1303).

Three-dimensional data encoding device 1300 encodes the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, using the predicted attribute information. Forexample, three-dimensional data encoding device 1300 calculatesdifferential attribute information, the differential attributeinformation being a difference between the predicted attributeinformation and the attribute information on the three-dimensionalpoints included in the current three-dimensional data (S1304).Three-dimensional data encoding device 1300 next performs transformationand quantization on the calculated differential attribute information(S1305).

Lastly, three-dimensional data encoding device 1300 encodes (e.g.entropy encodes) the differential position information and the quantizeddifferential attribute information (S1036). In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the differential position information andthe differential attribute information.

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data encoding device 1300 doesnot need to perform steps S1302, S1304, and S1305. Three-dimensionaldata encoding device 1300 may also perform only one of the encoding ofthe position information on the three-dimensional points and theencoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 49 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1301 and S1303) and the processes withrespect to the attribute information (S1302, S1304, and S1305) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

With the above, three-dimensional data encoding device 1300 according tothe present embodiment generates predicted position information usingposition information on three-dimensional points included inthree-dimensional reference data associated with a time different from atime associated with current three-dimensional data; and encodesdifferential position information, which is a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data.This makes it possible to improve encoding efficiency since it ispossible to reduce the amount of data of the encoded signal.

Three-dimensional data encoding device 1300 according to the presentembodiment generates predicted attribute information using attributeinformation on three-dimensional points included in three-dimensionalreference data; and encodes differential attribute information, which isa difference between the predicted attribute information and theattribute information on the three-dimensional points included in thecurrent three-dimensional data. This makes it possible to improveencoding efficiency since it is possible to reduce the amount of data ofthe encoded signal.

For example, three-dimensional data encoding device 1300 includes aprocessor and memory. The processor uses the memory to perform the aboveprocesses.

FIG. 48 is a flowchart of the inter prediction process performed bythree-dimensional data decoding device 1400.

Three-dimensional data decoding device 1400 decodes (e.g. entropydecodes) the differential position information and the differentialattribute information from the encoded signal (encoded bitstream)(S1401).

Three-dimensional data decoding device 1400 decodes, from the encodedsignal, an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data.Three-dimensional data decoding device 1400 encodes RT information thatindicates contents of the rotation and translation process, Note thatthree-dimensional data decoding device 1400 may decode the RTinformation when the RT flag indicates to apply the rotation andtranslation process, and does not need to decode the RT information whenthe RT flag indicates not to apply the rotation and translation process.

Three-dimensional data decoding device 1400 next performs inversetransformation and inverse quantization on the decoded differentialattribute information (S1402).

Three-dimensional data decoding device 1400 next generates predictedposition information (e.g. predicted volume) using the positioninformation on the three-dimensional points included in thethree-dimensional reference data (e.g. reference space) associated witha time different from a time associated with the currentthree-dimensional data (e.g. decoding target space) (S1403). To bespecific, three-dimensional data decoding device 1400 generates thepredicted position information by applying a rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data.

More specifically, when the RT flag indicates to apply the rotation andtranslation process, three-dimensional data decoding device 1400 appliesthe rotation and translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata indicated in the RT information. In contrast, when the RT flagindicates not to apply the rotation and translation process,three-dimensional data decoding device 1400 does not apply the rotationand translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data decoding device 1400 may perform therotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Notethat three-dimensional data decoding device 1400 may perform therotation and translation process, and the generating of the predictedposition information in the same unit.

Three-dimensional data decoding device 1400 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41, the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

Three-dimensional data decoding device 1400 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1404).

Three-dimensional data decoding device 1400 next restores the positioninformation on the three-dimensional points included in the currentthree-dimensional data, by decoding encoded position informationincluded in an encoded signal, using the predicted position information.The encoded position information here is the differential positioninformation. Three-dimensional data decoding device 1400 restores theposition information on the three-dimensional points included in thecurrent three-dimensional data, by adding the differential positioninformation to the predicted position information (S1405).

Three-dimensional data decoding device 1400 restores the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, by decoding encoded attribute informationincluded in an encoded signal, using the predicted attributeinformation. The encoded attribute information here is the differentialposition information. Three-dimensional data decoding device 1400restores the attribute information on the three-dimensional pointsincluded in the current three-dimensional data, by adding thedifferential attribute information to the predicted attributeinformation (S1406).

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data decoding device 1400 doesnot need to perform steps S1402, S1404, and S1406. Three-dimensionaldata decoding device 1400 may also perform only one of the decoding ofthe position information on the three-dimensional points and thedecoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 50 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1403 and S1405) and the processes withrespect to the attribute information (S1402, S1404, and S1406) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

Embodiment 8

Information of a three-dimensional point cloud includes geometryinformation (geometry) and attribute information (attribute). Geometryinformation includes coordinates (x-coordinate, y-coordinate,z-coordinate) with respect to a certain point. When geometry informationis encoded, a method of representing the position of each ofthree-dimensional points in octree representation and encoding theoctree information to reduce a code amount is used instead of directlyencoding the coordinates of the three-dimensional point.

On the other hand, attribute information includes informationindicating, for example, color information (RGB, YUV, etc.) of eachthree-dimensional point, a reflectance, and a normal vector. Forexample, a three-dimensional data encoding device is capable of encodingattribute information using an encoding method different from a methodused to encode geometry information.

In the present embodiment, a method of encoding attribute information isexplained. It is to be noted that, in the present embodiment, the methodis explained based on an example case using integer values as values ofattribute information. For example, when each of RGB or YUV colorcomponents is of an 8-bit accuracy the color component is an integervalue in a range from 0 to 255. When a reflectance value is of 10-bitaccuracy the reflectance value is an integer in a range from 0 to 1023.It is to be noted that, when the bit accuracy of attribute informationis a decimal accuracy, the three-dimensional data encoding device maymultiply the value by a scale value to round it to an integer value sothat the value of the attribute information becomes an integer value. Itis to be noted that the three-dimensional data encoding device may addthe scale value to, for example, a header of a bitstream.

As a method of encoding attribute information of a three-dimensionalpoint, it is conceivable to calculate a predicted value of the attributeinformation of the three-dimensional point and encode a difference(prediction residual) between the original value of the attributeinformation and the predicted value. For example, when the value ofattribute information at three-dimensional point p is Ap and a predictedvalue is Pp, the three-dimensional data encoding device encodesdifferential absolute value Diffp=|Ap−Pp|. In this case, whenhighly-accurate predicted value Pp can be generated, differentialabsolute value Diffp is small. Thus, for example, it is possible toreduce the code amount by entropy encoding differential absolute valueDiffp using a coding table that reduces an occurrence bit count morewhen differential absolute value Diffp is smaller.

As a method of generating a prediction value of attribute information,it is conceivable to use attribute information of a referencethree-dimensional point that is another three-dimensional point whichneighbors a current three-dimensional point to be encoded. Here, areference three-dimensional point is a three-dimensional point in arange of a predetermined distance from the current three-dimensionalpoint. For example, when there are current three-dimensional pointp=(x1, y1, z1) and three-dimensional point q=(x2, y2, z2), thethree-dimensional data encoding device calculates Euclidean distance d(p, q) between three-dimensional point p and three-dimensional point qrepresented by (Equation A1).

d(p,q)=√{square root over ((x1−y1)²+(x2−y2)²+(x3−y3)²)}  (Equation A1)

The three-dimensional data encoding device determines that the positionof three-dimensional point q is closer to the position of currentthree-dimensional point p when Euclidean distance d (p, q) is smallerthan predetermined threshold value THd, and determines to use the valueof the attribute information of three-dimensional point q to generate apredicted value of the attribute information of currentthree-dimensional point p. It is to be noted that the method ofcalculating the distance may be another method, and a Mahalanobisdistance or the like may be used. In addition, the three-dimensionaldata encoding device may determine not to use, in prediction processing,any three-dimensional point outside the predetermined range of distancefrom the current three-dimensional point. For example, whenthree-dimensional point r is present, and distance d (p, r) betweencurrent three-dimensional point p and three-dimensional point r islarger than or equal to threshold value THd, the three-dimensional dataencoding device may determine not to use three-dimensional point r forprediction. It is to be noted that the three-dimensional data encodingdevice may add the information indicating threshold value THd to, forexample, a header of a bitstream.

FIG. 51 is a diagram illustrating an example of three-dimensionalpoints. In this example, distance d (p, q) between currentthree-dimensional point p and three-dimensional point q is smaller thanthreshold value THd. Thus, the three-dimensional data encoding devicedetermines that three-dimensional point q is a referencethree-dimensional point of current three-dimensional point p, anddetermines to use the value of attribute information Aq ofthree-dimensional point q to generate predicted value Pp of attributeinformation Ap of current three-dimensional point p.

In contrast, distance d (p, r) between current three-dimensional point pand three-dimensional point r is larger than or equal to threshold valueTHd. Thus, the three-dimensional data encoding device determines thatthree-dimensional point r is not any reference three-dimensional pointof current three-dimensional point p, and determines not to use thevalue of attribute information. Ar of three-dimensional point r togenerate predicted value Pp of attribute information Ap of currentthree-dimensional point p.

In addition, when encoding the attribute information of the currentthree-dimensional point using a predicted value, the three-dimensionaldata encoding device uses a three-dimensional point whose attributeinformation has already been encoded and decoded, as a referencethree-dimensional point. Likewise, when decoding the attributeinformation of a current three-dimensional point to be decoded, thethree-dimensional data decoding device uses a three-dimensional pointwhose attribute information has already been decoded, as a referencethree-dimensional point. In this way, it is possible to generate thesame predicted value at the time of encoding and decoding. Thus, abitstream of the three-dimensional point generated by the encoding canbe decoded correctly at the decoding side.

Furthermore, when encoding attribute information of each ofthree-dimensional points, it is conceivable to classify thethree-dimensional point into one of a plurality of layers using geometryinformation of the three-dimensional point and then encode the attributeinformation. Here, each of the layers classified is referred to as aLevel of Detail (LoD). A method of generating LoDs is explained withreference to FIG. 52.

First, the three-dimensional data encoding device selects initial pointa0 and assigns initial point a0 to LoD0. Next, the three-dimensionaldata encoding device extracts point a1 distant from point a0 more thanthreshold value Thres_LoD[0] of LoD0 and assigns point a1 to LoD0. Next,the three-dimensional data encoding device extracts point a2 distantfrom point a1 more than threshold value Thres_LoD[0] of LoD0 and assignspoint a2 to LoD0. In this way, the three-dimensional data encodingdevice configures LoD0 in such a manner that the distance between thepoints in LoD0 is larger than threshold value Thres_LoD[0].

Next, the three-dimensional data encoding device selects point b0 whichhas not yet been assigned to any LoD and assigns point b0 to LoD1. Next,the three-dimensional data encoding device extracts point b1 which isdistant from point b0 more than threshold value Thres_LoD[1] of LoD1 andwhich has not yet been assigned to any LoD, and assigns point b1 toLoD1. Next, the three-dimensional data encoding device extracts point b2which is distant from point b1 more than threshold value Thres_LoD[1] ofLoD1 and which has not yet been assigned to any LoD, and assigns pointb2 to LoD1. In this way, the three-dimensional data encoding deviceconfigures LoD1 in such a manner that the distance between the points inLoD1 is larger than threshold value Thres_LoD[1].

Next, the three-dimensional data encoding device selects point c0 whichhas not yet been assigned to any LoD and assigns point c0 to LoD2. Next,the three-dimensional data encoding device extracts point c1 which isdistant from point c0 more than threshold value Thres_LoD[2] of LoD2 andwhich has not yet been assigned to any LoD, and assigns point c1 toLoD2. Next, the three-dimensional data encoding device extracts point c2which is distant from point c1 more than threshold value Thres_LoD[2] ofLoD2 and which has not yet been assigned to any LoD, and assigns pointc2 to LoD2. In this way the three-dimensional data encoding deviceconfigures LoD2 in such a manner that the distance between the points inLoD2 is larger than threshold value Thres_LoD[2]. For example, asillustrated in FIG. 53, threshold values Thres_LoD[0], Thres_LoD[1], andThres_LoD[2] of respective LoDs are set.

In addition, the three-dimensional data encoding device may add theinformation indicating the threshold value of each LoD to, for example,a header of a bitstream. For example, in the case of the exampleillustrated in FIG. 53, the three-dimensional data encoding device mayadd threshold values Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] ofrespective LoDs to a header.

Alternatively, the three-dimensional data encoding device may assign allthree-dimensional points which have not yet been assigned to any LoD inthe lowermost-layer LoD. In this case, the three-dimensional dataencoding device is capable of reducing the code amount of the header bynot assigning the threshold value of the lowermost-layer LoD to theheader. For example, in the case of the example illustrated in FIG. 53,the three-dimensional data encoding device assigns threshold valuesThres_LoD[0] and Thres_LoD[1] to the header, and does not assignThres_LoD[2] to the header. In this case, the three-dimensional dataencoding device may estimate value 0 of Thres_LoD[2]. In addition, thethree-dimensional data encoding device may add the number of LoDs to aheader. In this way, the three-dimensional data encoding device iscapable of determining the lowermost-layer LoD using the number of LoDs.

In addition, setting threshold values for the respective layers LoDs insuch a manner that a larger threshold value is set to a higher layermakes a higher layer (layer closer to LoD0) to have a sparse point cloud(sparse) in which three-dimensional points are more distant and makes alower layer to have a dense point cloud (dense) in whichthree-dimensional points are closer. It is to be noted that, in anexample illustrated in FIG. 53, LoD0 is the uppermost layer.

In addition, the method of selecting an initial three-dimensional pointat the time of setting each LoD may depend on an encoding order at thetime of geometry information encoding. For example, thethree-dimensional data encoding device configures LoD0 by selecting thethree-dimensional point encoded first at the time of the geometryinformation encoding as initial point a0 of LoD0, and selecting point a1and point a2 from initial point a0 as the origin. The three-dimensionaldata encoding device then may select the three-dimensional point whosegeometry information has been encoded at the earliest time amongthree-dimensional points which do not belong to LoD0, as initial pointb0 of LoD1. In other words, the three-dimensional data encoding devicemay select the three-dimensional point whose geometry information hasbeen encoded at the earliest time among three-dimensional points whichdo not belong to layers (LoD0 to LoDn−1) above LoDn, as initial point n0of LoDn. In this way, the three-dimensional data encoding device iscapable of configuring the same LoD as in encoding by using, indecoding, the initial point selecting method similar to the one used inthe encoding, which enables appropriate decoding of a bitstream. Morespecifically, the three-dimensional data encoding device selects thethree-dimensional point whose geometry information has been decoded atthe earliest time among three-dimensional points which do not belong tolayers above LoDn, as initial point n0 of LoDn.

Hereinafter, a description is given of a method of generating thepredicted value of the attribute information of each three-dimensionalpoint using information of LoDs. For example, when encodingthree-dimensional points starting with the three-dimensional pointsincluded in LoD0, the three-dimensional data encoding device generatescurrent three-dimensional points which are included in LoD1 usingencoded and decoded (hereinafter also simply referred to as “encoded”)attribute information included in LoD0 and LoD1. In this way, thethree-dimensional data encoding device generates a predicted value ofattribute information of each three-dimensional point included in LoDnusing encoded attribute information included in LoDn′(n′≤n). In otherwords, the three-dimensional data encoding device does not use attributeinformation of each of three-dimensional points included in any layerbelow LoDn to calculate a predicted value of attribute information ofeach of the three-dimensional points included in LoDn.

For example, the three-dimensional data encoding device calculates anaverage of attribute information of N or less three dimensional pointsamong encoded three-dimensional points surrounding a currentthree-dimensional point to be encoded, to generate a predicted value ofattribute information of the current three-dimensional point. Inaddition, the three-dimensional data encoding device may add value N to,for example, a header of a bitstream. It is to be noted that thethree-dimensional data encoding device may change value N for eachthree-dimensional point, and may add value N for each three-dimensionalpoint. This enables selection of appropriate N for eachthree-dimensional point, which makes it possible to increase theaccuracy of the predicted value. Accordingly it is possible to reducethe prediction residual. Alternatively, the three-dimensional dataencoding device may add value N to a header of a bitstream, and may fixthe value indicating N in the bitstream. This eliminates the need toencode or decode value N for each three-dimensional point, which makesit possible to reduce the processing amount. In addition, thethree-dimensional data encoding device may encode the values of Nseparately for each LoD. In this way, it is possible to increase thecoding efficiency by selecting appropriate N for each LoD.

Alternatively, the three-dimensional data encoding device may calculatea predicted value of attribute information of three-dimensional pointbased on weighted average values of attribute information of encoded Nneighbor three-dimensional points. For example, the three-dimensionaldata encoding device calculates weights using distance informationbetween a current three-dimensional point and each of N neighborthree-dimensional points.

When encoding value N for each LoD, for example, the three-dimensionaldata encoding device sets larger value N to a higher layer LoD, and setssmaller value N to a lower layer LoD. The distance betweenthree-dimensional points belonging to a higher layer LoD is large, thereis a possibility that it is possible to increase the prediction accuracyby setting large value N, selecting a plurality of neighborthree-dimensional points, and averaging the values. Furthermore, thedistance between three-dimensional points belonging to a lower layer LoDis small, it is possible to perform efficient prediction while reducingthe processing amount of averaging by setting smaller value N.

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values. As described above, the predicted value ofpoint P included in LoDN is generated using encoded neighbor point P′included in LoDN′ (N′≤N). Here, neighbor point P′ is selected based onthe distance from point P. For example, the predicted value of attributeinformation of point b2 illustrated in FIG. 54 is generated usingattribute information of each of points a0, a1, b0, and b1.

Neighbor points to be selected vary depending on the values of Ndescribed above. For example, in the case of N=5, a0, a1, a2, b0, and b1are selected as neighbor points. In the case of N=4, a0, a1, a2, and b1are selected based on distance information.

The predicted value is calculated by distance-dependent weightedaveraging. For example, in the example illustrated in FIG. 54, predictedvalue a2 p of point a2 is calculated by weighted averaging of attributeinformation of each of point a0 and a1, as represented by (Equation A2)and (Equation A3). It is to be noted that A1 is an attribute informationvalue of ai.

$\begin{matrix}{{a\; 2\; p} = {\sum\limits_{i = 0}^{1}{w_{i} \times A_{i}}}} & \left( {{Equation}\mspace{14mu} A\; 2} \right) \\{w_{i} = \frac{\frac{1}{d\left( {{a\; 2},{a\; i}} \right)}}{\sum_{j = 0}^{1}\frac{1}{d\left( {{a\; 2},{aj}} \right)}}} & \left( {{Equation}\mspace{14mu} A\; 3} \right)\end{matrix}$

In addition, predicted value b2 p of point b2 is calculated by weightedaveraging of attribute information of each of point a0, a1, a2, b0, andb1, as represented by (Equation A4) and (Equation A6). It is to be notedthat B_(i) is an attribute information value of bi.

$\begin{matrix}{{b\; 2\; p} = {{\sum_{i = 0}^{2}{{wa}_{i} \times A_{i}}} + {\sum_{i = 0}^{1}{{wb}_{i} \times B_{i}}}}} & \left( {{Equation}\mspace{14mu} A\; 4} \right) \\{{wa}_{i} = \frac{\frac{1}{d\left( {{b\; 2},{ai}} \right)}}{{\sum_{j = 0}^{2}\frac{1}{d\left( {{b\; 2},{aj}} \right)}} + {\sum_{j = 0}^{1}\frac{1}{d\left( {{b\; 2},{bj}} \right)}}}} & \left( {{Equation}\mspace{14mu} A\; 5} \right) \\{{wb}_{i} = \frac{\frac{1}{d\left( {{b\; 2},{bi}} \right)}}{{\sum_{j = 0}^{2}\frac{1}{d\left( {{b\; 2},{aj}} \right)}} + {\sum_{j = 0}^{1}\frac{1}{d\left( {{b\; 2},{bj}} \right)}}}} & \left( {{Equation}\mspace{14mu} A\; 6} \right)\end{matrix}$

In addition, the three-dimensional data encoding device may calculate adifference value (prediction residual) generated from the value ofattribute information of a three-dimensional point and neighbor points,and may quantize the calculated prediction residual. For example, thethree-dimensional data encoding device performs quantization by dividingthe prediction residual by a quantization scale (also referred to as aquantization step). In this case, an error (quantization error) whichmay be generated by quantization reduces as the quantization scale issmaller. In the other case where the quantization scale is larger, theresulting quantization error is larger.

It is to be noted that the three-dimensional data encoding device maychange the quantization scale to be used for each LoD. For example, thethree-dimensional data encoding device reduces the quantization scalemore for a higher layer, and increases the quantization scale more for alower layer. The value of attribute information of a three-dimensionalpoint belonging to a higher layer may be used as a predicted value ofattribute information of a three-dimensional point belonging to a lowerlayer. Thus, it is possible to increase the coding efficiency byreducing the quantization scale for the higher layer to reduce thequantization error that can be generated in the higher layer and toincrease the prediction accuracy of the predicted value. It is to benoted that the three-dimensional data encoding device may add thequantization scale to be used for each LoD to, for example, a header. Inthis way the three-dimensional data encoding device can decode thequantization scale correctly, thereby appropriately decoding thebitstream.

In addition, the three-dimensional data encoding device may convert asigned integer value (signed quantized value) which is a quantizedprediction residual into an unsigned integer value (unsigned quantizedvalue). This eliminates the need to consider occurrence of a negativeinteger when entropy encoding the prediction residual. It is to be notedthat the three-dimensional data encoding device does not always need toconvert a signed integer value into an unsigned integer value, and, forexample, that the three-dimensional data encoding device may entropyencode a sign bit separately.

The prediction residual is calculated by subtracting a prediction valuefrom the original value. For example, as represented by (Equation A7),prediction residual a2 r of point a2 is calculated by subtractingpredicted value a2 p of point a2 from value A₂ of attribute informationof point a2. As represented by (Equation A8), prediction residual b2 rof point b2 is calculated by subtracting predicted value b2 p of pointb2 from value B₂ of attribute information of point b2.

a2r=A ₂ −a2p   (Equation A7)

b2r=B ₂ −b2p   (Equation A8)

In addition, the prediction residual is quantized by being divided by aQuantization Step (QS). For example, quantized value a2 q of point a2 iscalculated according to (Equation A9). Quantized value b2 q of point b2is calculated according to (Equation A10). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2q=a2r/QS_LoD0   (Equation A9)

b2q=b2r/QS_LoD1   (Equation A10)

In addition, the three-dimensional data encoding device converts signedinteger values which are quantized values as indicated below intounsigned integer values as indicated below. When signed integer value a2q is smaller than 0, the three-dimensional data encoding device setsunsigned integer value a2 u to −1−(2×a2 q). When signed integer value a2q is 0 or more, the three-dimensional data encoding device sets unsignedinteger value a2 u to 2×a2 q.

Likewise, when signed integer value b2 q is smaller than 0, thethree-dimensional data encoding device sets unsigned integer value b2 uto −1−(2×b2 q). When signed integer value a2 q is 0 or more, thethree-dimensional data encoding device sets unsigned integer value b2 uto 2×b2 q.

In addition, the three-dimensional data encoding device may encode thequantized prediction residual (unsigned integer value) by entropyencoding. For example, the three-dimensional data encoding device maybinarize the unsigned integer value and then apply binary arithmeticencoding to the binary value.

It is to be noted that, in this case, the three-dimensional dataencoding device may switch binarization methods according to the valueof a prediction residual. For example, when prediction residual pu issmaller than threshold value R_TH, the three-dimensional data encodingdevice binarizes prediction residual pu using a fixed bit count requiredfor representing threshold value R_TH. In addition, when predictionresidual pu is larger than or equal to threshold value R_TH, thethree-dimensional data encoding device binarizes the binary data ofthreshold value R_TH and the value of (pu−R_TH), usingexponential-Golomb coding, or the like.

For example, when threshold value R_TH is 63 and prediction residual puis smaller than. 63, the three-dimensional data encoding devicebinarizes prediction residual pu using 6 bits. When prediction residualpu is larger than or equal to 63, the three-dimensional data encodingdevice performs arithmetic encoding by binarizing the binary data(111111) of threshold value and (pu−63) using exponential-Golomb coding.

In a more specific example, when prediction residual pu is 32, thethree-dimensional data encoding device generates 6-bit binary data(100000), and arithmetic encodes the bit sequence. In addition, whenprediction residual pu is 66, the three-dimensional data encoding devicegenerates binary data (111111) of threshold value R_TH and a bitsequence (00100) representing value 3 (66−63) using exponential-Golombcoding, and arithmetic encodes the bit sequence (111111+00100),

In this way the three-dimensional data encoding device can performencoding while preventing a binary bit count from increasing abruptly inthe case where a prediction residual becomes large by switchingbinarization methods according to the magnitude of the predictionresidual. It is to be noted that the three-dimensional data encodingdevice may add threshold value R_TH to, for example, a header of abitstream.

For example, in the case where encoding is performed at a high bit rate,that is, when a quantization scale is small, a small quantization errorand a high prediction accuracy are obtained. As a result, a predictionresidual may not be large. Thus, in this case, the three-dimensionaldata encoding device sets large threshold value R_TH. This reduces thepossibility that the binary data of threshold value R_TH is encoded,which increases the coding efficiency. In the opposite case whereencoding is performed at a low bit rate, that is, when a quantizationscale is large, a large quantization error and a low prediction accuracyare obtained. As a result, a prediction residual may be large. Thus, inthis case, the three-dimensional data encoding device sets smallthreshold value R_TH. In this way, it is possible to prevent abruptincrease in bit length of binary data.

In addition, the three-dimensional data encoding device may switchthreshold value R_TH for each LoD, and may add threshold value R_TH foreach LoD to, for example, a header. In other words, thethree-dimensional data encoding device may switch binarization methodsfor each LoD. For example, since distances between three-dimensionalpoints are large in a higher layer, a prediction accuracy is low, whichmay increase a prediction residual. Thus, the three-dimensional dataencoding device prevents abrupt increase in bit length of binary data bysetting small threshold value R_TH to the higher layer. In addition,since distances between three-dimensional points are small in a lowerlayer, a prediction accuracy is high, which may reduce a predictionresidual. Thus, the three-dimensional data encoding device increases thecoding efficiency by setting large threshold value to the lower layer.

FIG. 55 is a diagram indicating examples of exponential-Golomb codes.The diagram indicates the relationships between pre-binarization values(non-binary values) and post-binarization bits (codes). It is to benoted that 0 and 1 indicated in FIG. 55 may be inverted.

The three-dimensional data encoding device applies arithmetic encodingto the binary data of prediction residuals. In this way, the codingefficiency can be increased. It is to be noted that, in the applicationof the arithmetic encoding, there is a possibility that occurrenceprobability tendencies of 0 and 1 in each bit vary, in binary data,between an n-bit code which is a part binarized by n bits and aremaining code which is a part binarized using exponential-Golombcoding. Thus, the three-dimensional data encoding device may switchmethods of applying arithmetic encoding between the n-bit code and theremaining code.

For example, the three-dimensional data encoding device performsarithmetic encoding on the n-bit code using one or more coding tables(probability tables) different for each bit. At this time, thethree-dimensional data encoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional dataencoding device performs arithmetic encoding using one coding table forfirst bit b0 in an n-bit code. The three-dimensional data encodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata encoding device switches coding tables to be used for arithmeticencoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data encoding device uses four coding tables for thenext bit b2. The three-dimensional data encoding device switches codingtables to be used for arithmetic encoding of bit b2 according to thevalues (in a range from 0 to 3) of b0 and b1.

In this way the three-dimensional data encoding device uses 2^(n−1)coding tables when arithmetic encoding each bit bn−1 in n-bit code. Thethree-dimensional data encoding device switches coding tables to be usedaccording to the values (occurrence patterns) of bits before bn−1. Inthis way, the three-dimensional data encoding device can use codingtables appropriate for each bit, and thus can increase the codingefficiency.

It is to be noted that the three-dimensional data encoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data encoding device may switch coding tablesaccording to the values (occurrence patterns) of m bits (m<n−1) beforebn−1 when arithmetic encoding each bit bn−1. In this way it is possibleto increase the coding efficiency while reducing the number of codingtables to be used for each bit. It is to be noted that thethree-dimensional data encoding device may update the occurrenceprobabilities of 0 and 1 in each coding table according to the values ofbinary data occurred actually. In addition, the three-dimensional dataencoding device may fix the occurrence probabilities of 0 and 1 incoding tables for some bit(s). In this way it is possible to reduce thenumber of updates of occurrence probabilities, and thus to reduce theprocessing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one table (CTb0). Coding tables for b1 are two tables(CTb10 and CTb11). Coding tables to be used are switched according tothe value (0 or 1) of b0. Coding tables for b2 are four tables (CTb20,CTb21, CTb22, and CTb23). Coding tables to be used are switchedaccording to the values (in the range from 0 to 3) of b0 and b1. Codingtables for bn−1 are 2^(n−1) tables (CTbn0, CTbn1, . . . , CTbn(2^(n−1)−1)). Coding tables to be used are switched according to thevalues (in a range from 0 to 2^(n−1)−1) of b0, b1, . . . , bn−2.

It is to be noted that the three-dimensional data encoding device mayapply to an n-bit code, arithmetic encoding (m=2^(n)) by m-ary that setsthe value in the range from 0 to 2^(n)−1 without binarization. When thethree-dimensional data encoding device arithmetic encodes an n-bit codeby an m-ary, the three-dimensional data decoding device may reconstructthe n-bit code by arithmetic decoding the m-ary.

FIG. 56 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 56,each remaining code which is a part binarized using exponential-Golombcoding includes a prefix and a suffix. For example, thethree-dimensional data encoding device switches coding tables betweenthe prefix and the suffix. In other words, the three-dimensional dataencoding device arithmetic encodes each of bits included in the prefixusing coding tables for the prefix, and arithmetic encodes each of bitsincluded in the suffix using coding tables for the suffix.

It is to be noted that the three-dimensional data encoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred actually. In addition,the three-dimensional data encoding device may fix the occurrenceprobabilities of 0 and 1 in one of coding tables. In this way, it ispossible to reduce the number of updates of occurrence probabilities,and thus to reduce the processing amount. For example, thethree-dimensional data encoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

In addition, the three-dimensional data encoding device decodes aquantized prediction residual by inverse quantization andreconstruction, and uses a decoded value which is the decoded predictionresidual for prediction of a current three-dimensional point to beencoded and the following three-dimensional point(s). More specifically,the three-dimensional data encoding device calculates an inversequantized value by multiplying the quantized prediction residual(quantized value) with a quantization scale, and adds the inversequantized value and a prediction value to obtain the decoded value(reconstructed value).

For example, quantized value a2iq of point a2 is calculated usingquantized value a2 q of point a2 according to (Equation A11). Inversequantized value b2 iq of point b2 q is calculated using quantized valueb2 q of point b2 according to (Equation A12). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2iq=a2q×QS_LoD0   (Equation A11)

b2iq=b2q×QS_LoD1   (Equation A12)

For example, as represented by (Equation A13), decoded value a2 rec ofpoint a2 is calculated by adding inverse quantization value a2 iq ofpoint a2 to predicted value a2 p of point a2. As represented by(Equation A14), decoded value b2 rec of point b2 is calculated by addinginverse quantized value b2 iq of point b2 to predicted value b2 p ofpoint b2.

a2rec=a2iq+a2p   (Equation A13)

b2rec=b2iq+b2p   (Equation A14)

Hereinafter, a syntax example of a bitstream according to the presentembodiment is described. FIG. 57 is a diagram indicating the syntaxexample of an attribute header (attribute_header) according to thepresent embodiment. The attribute header is header information ofattribute information. As indicated in FIG. 57, the attribute headerincludes the number of layers information (NumLoD), the number ofthree-dimensional points information (NumOfPoint[i]), a layer thresholdvalue (Thres_LoD[i]), the number of neighbor points information(NumNeighborPoint[i]), a prediction threshold value (THd[i]), aquantization scale (QS[i]), and a binarization threshold value(R_TH[i]).

The number of layers information (NumLoD) indicates the number of LoDsto be used.

The number of three-dimensional points information (NumOfPoint[i])indicates the number of three-dimensional points belonging to layer i.It is to be noted that the three-dimensional data encoding device mayadd, to another header, the number of three-dimensional pointsinformation indicating the total number of three-dimensional points. Inthis case, the three-dimensional data encoding device does not need toadd, to a header, NumOfPoint [NumLoD−1] indicating the number ofthree-dimensional points belonging to the lowermost layer. In this case,the three-dimensional data decoding device is capable of calculatingNumOfPoint [NumLoD−1] according to (Equation A15). In this case, it ispossible to reduce the code amount of the header.

$\begin{matrix}{{{NumOfPoint}\left\lbrack {{NumLoD} - 1} \right\rbrack} = {{AllNumOfPoint} - {\sum\limits_{j = 0}^{{NumLoD} - 2}{{NumOfPoint}\lbrack j\rbrack}}}} & \left( {{Equation}\mspace{14mu} A\; 15} \right)\end{matrix}$

The layer threshold value (Thres_LoD[i]) is a threshold value to be usedto set layer i. The three-dimensional data encoding device and thethree-dimensional data decoding device configure LoDi in such a mannerthat the distance between points in LoDi becomes larger than thresholdvalue Thres_LoD[i]. The three-dimensional data encoding device does notneed to add the value of ThresLoD [NumLoD−1] (lowermost layer) to aheader. In this case, the three-dimensional data decoding device mayestimate 0 as the value of Thres_LoD [NumLoD−1]. In this case, it ispossible to reduce the code amount of the header.

The number of neighbor points information (NumNeighborPoint[i])indicates the upper limit value of the number of neighbor points to beused to generate a predicted value of a three-dimensional pointbelonging to layer i. The three-dimensional data encoding device maycalculate a predicted value using the number of neighbor points M whenthe number of neighbor points M is smaller thanNumNeighborPoint[i](M<NumNeighborPoint[i]). Furthermore, when there isno need to differentiate the values of NumNeighborPoint[i] forrespective LoDs, the three-dimensional data encoding device may add apiece of the number of neighbor points information (NumNeighborPoint) tobe used in all LoDs to a header.

The prediction threshold value (THd[i]) indicates the upper limit valueof the distance between a current three-dimensional point to be encodedor decoded in layer i and each of neighbor three-dimensional points tobe used to predict the current three-dimensional point. Thethree-dimensional data encoding device and the three-dimensional datadecoding device do not use, for prediction, any three-dimensional pointdistant from the current three-dimensional point over THd[i]. It is tobe noted that, when there is no need to differentiate the values ofTHd[i] for respective LoDs, the three-dimensional data encoding devicemay add a single prediction threshold value (THd) to be used in all LoDsto a header.

The quantization scale (QS[i]) indicates a quantization scale to be usedfor quantization and inverse quantization in layer i.

The binarization threshold value (R_TH[i]) is a threshold value forswitching binarization methods of prediction residuals ofthree-dimensional points belonging to layer i. For example, thethree-dimensional data encoding device binarizes prediction residual puusing a fixed bit count when a prediction residual is smaller thanthreshold value R_TH, and binarizes the binary data of threshold valueR_TH and the value of (pu-R_TH) using exponential-Golomb coding when aprediction residual is larger than or equal to threshold value R_TH. Itis to be noted that, when there is no need to switch the values ofR_TH[i] between LoDs, the three-dimensional data encoding device may adda single binarization threshold value (R_TH) to be used in all LoDs to aheader.

It is to be noted that R_TH[i] may be the maximum value which can berepresented by n bits. For example, R_TH is 63 in the case of 6 bits,and R_TH is 255 in the case of 8 bits. Alternatively, thethree-dimensional data encoding device may encode a bit count instead ofencoding the maximum value which can be represented by n bits as abinarization threshold value. For example, the three-dimensional dataencoding device may add value 6 in the case of R_TH[i]=63 to a header,and may add value 8 in the case of R_TH[i]=255 to a header.Alternatively, the three-dimensional data encoding device may define theminimum value (minimum bit count) representing R_TH[i], and add arelative bit count from the minimum value to a header. For example, thethree-dimensional data encoding device may add value 0 to a header whenR_TH[i]=63 is satisfied and the minimum bit count is 6, and may addvalue 2 to a header when R_TH[i]=255 is satisfied and the minimum bitcount is 6.

Alternatively, the three-dimensional data encoding device may entropyencode at least one of NumLoD, Thres_LoD[i], NumNeighborPoint[i],THd[i], QS[i], and R_TH[i], and add the entropy encoded one to a header.For example, the three-dimensional data encoding device may binarizeeach value and perform arithmetic encoding on the binary value. Inaddition, the three-dimensional data encoding device may encode eachvalue using a fixed length in order to reduce the processing amount.

Alternatively, the three-dimensional data encoding device does notalways need to add at least one of NumLoD, Thres_LoD[i],NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to a header. Forexample, at least one of these values may be defined by a profile or alevel in a standard, or the like. In this way it is possible to reducethe bit amount of the header;

FIG. 58 is a diagram indicating the syntax example of attribute data(attribute_data) according to the present embodiment. The attribute dataincludes encoded data of the attribute information of a plurality ofthree-dimensional points. As indicated in. FIG. 58, the attribute dataincludes an n-bit code and a remaining code.

The n-bit code is encoded data of a prediction residual of a value ofattribute information or a part of the encoded data. The bit length ofthe n-bit code depends on value R_TH[i]. For example, the bit length ofthe n-bit code is 6 bits when the value indicated by R_TH[i] is 63, thebit length of the n-bit code is 8 bits when the value indicated byR_TH[i] is 255.

The remaining code is encoded data encoded using exponential-Golombcoding among encoded data of the prediction residual of the value of theattribute information. The remaining code is encoded or decoded when thevalue of the n-bit code is equal to R_TH[i]. The three-dimensional datadecoding device decodes the prediction residual by adding the value ofthe n-bit code and the value of the remaining code. It is to be notedthat the remaining code does not always need to be encoded or decodedwhen the value of the n-bit code is not equal to R_TH[i].

Hereinafter, a description is given of a flow of processing in thethree-dimensional data encoding device. FIG. 59 is a flowchart of athree-dimensional data encoding process performed by thethree-dimensional data encoding device.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3001). For example, the three-dimensional dataencoding is performed using octree representation.

When the positions of three-dimensional points changed by quantization,etc, after the encoding of the geometry information, thethree-dimensional data encoding device re-assigns attribute informationof the original three-dimensional points to the post-changethree-dimensional points (S3002). For example, the three-dimensionaldata encoding device interpolates values of attribute informationaccording to the amounts of change in position to re-assign theattribute information. For example, the three-dimensional data encodingdevice detects pre-change N three-dimensional points closer to thepost-change three-dimensional positions, and performs weighted averagingof the values of attribute information of the N three-dimensionalpoints. For example, the three-dimensional data encoding devicedetermines weights based on distances from the post-changethree-dimensional positions to the respective N three-dimensionalpositions in weighted averaging. The three-dimensional data encodingdevice then determines the values obtained through the weightedaveraging to be the values of the attribute information of thepost-change three-dimensional points. When two or more of thethree-dimensional points are changed to the same three-dimensionalposition through quantization, etc., the three-dimensional data encodingdevice may assign the average value of the attribute information of thepre-change two or more three-dimensional points as the values of theattribute information of the post-change three-dimensional points.

Next, the three-dimensional data encoding device encodes the attributeinformation (attribute) re-assigned (S3003). For example, when encodinga plurality of kinds of attribute information, the three-dimensionaldata encoding device may encode the plurality of kinds of attributeinformation in order. For example, when encoding colors and reflectancesas attribute information, the three-dimensional data encoding device maygenerate a bitstream added with the color encoding results and thereflectance encoding results after the color encoding results. It is tobe noted that the order of the plurality of encoding results ofattribute information to be added to a bitstream is not limited to theorder, and may be any order.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly it is possible to reduce theamount of processing by the three-dimensional data decoding device.Alternatively, the three-dimensional data encoding device may encode aplurality of kinds of attribute information in parallel, and mayintegrate the encoding results into a single bitstream. In this way thethree-dimensional data encoding device is capable of encoding theplurality of kinds of attribute information at high speed;

FIG. 60 is a flowchart of an attribute information encoding process(S3003). First, the three-dimensional data encoding device sets LoDs(S3011). In other words, the three-dimensional data encoding deviceassigns each of three-dimensional points to any one of the plurality ofLoDs.

Next, the three-dimensional data encoding device starts a loop for eachLoD (S3012). In other words, the three-dimensional data encoding deviceiteratively performs the processes of Steps from S3013 to S3021 for eachLoD.

Next, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3013). In other words, the three-dimensionaldata encoding device iteratively performs the processes of Steps fromS3014 to S3020 for each three-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point (S3014). Next, the three-dimensional dataencoding device calculates the weighted average of the values ofattribute information of the plurality of neighbor points, and sets theresulting value to predicted value P (S3015). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of the currentthree-dimensional point and the predicted value (S3016). Next, thethree-dimensional data encoding device quantizes the prediction residualto calculate a quantized value (S3017). Next, the three-dimensional dataencoding device arithmetic encodes the quantized value (S3018).

Next, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3019). Next,the three-dimensional data encoding device adds a prediction value tothe inverse quantized value to generate a decoded value (S3020). Next,the three-dimensional data encoding device ends the loop for eachthree-dimensional point (S3021). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3022).

Hereinafter, a description is given of a three-dimensional data decodingprocess in the three-dimensional data decoding device which decodes abitstream generated by the three-dimensional data encoding device.

The three-dimensional data decoding device generates decoded binary databy arithmetic decoding the binary data of the attribute information inthe bitstream generated by the three-dimensional data encoding device,according to the method similar to the one performed by thethree-dimensional data encoding device. It is to be noted that whenmethods of applying arithmetic encoding are switched between the part(n-bit code) binarized using n bits and the part (remaining code)binarized using exponential-Golomb coding in the three-dimensional dataencoding device, the three-dimensional data decoding device performsdecoding in conformity with the arithmetic encoding, when applyingarithmetic decoding.

For example, the three-dimensional data decoding device performsarithmetic decoding using coding tables (decoding tables) different foreach bit in the arithmetic decoding of the n-bit code. At this time, thethree-dimensional data decoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional datadecoding device performs arithmetic decoding using one coding table forfirst bit b0 in the n-bit code. The three-dimensional data decodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata decoding device switches coding tables to be used for arithmeticdecoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data decoding device uses four coding tables for thenext bit b2. The three-dimensional data decoding device switches codingtables to be used for arithmetic decoding of bit b2 according to thevalues (in the range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data decoding device uses 2^(n—1)coding tables when arithmetic decoding each bit bn−1 in the n-bit code.The three-dimensional data. decoding device switches coding tables to beused according to the values (occurrence patterns) of bits before bn−1.In this way the three-dimensional data decoding device is capable ofappropriately decoding a bitstream encoded at an increased codingefficiency using the coding tables appropriate for each bit.

It is to be noted that the three-dimensional data decoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data decoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic decoding each bit bn−1. In this way, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at the increased coding efficiency whilereducing the number of coding tables to be used for each bit. It is tobe noted that the three-dimensional data decoding device may update theoccurrence probabilities of 0 and 1 in each coding table according tothe values of binary data occurred actually. In addition, thethree-dimensional data decoding device may fix the occurrenceprobabilities of 0 and 1 in coding tables for some bit(s). In this way,it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one (CTb0). Coding tables for b1 are two tables (CTb10and CTb11). Coding tables to be used are switched according to the value(0 or 1) of b0. Coding tables for b2 are four tables (CTb20, CTb21,CTb22, and CTb23). Coding tables to be used according to the values (inthe range from 0 to 3) of b0 and b1. Coding tables for bn−1 are 2^(n−1)tables (CTbn0, CTbn1, . . . , CTbn (2^(n−1)−1)). Coding tables to beused are switched according to the values (in the range from 0 to2^(n−1)−1) of b0, b1, . . . , bn−2.

FIG. 61 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 61,the part (remaining part) binarized and encoded by the three-dimensionaldata encoding device using exponential-Golomb coding includes a prefixand a suffix. For example, the three-dimensional data decoding deviceswitches coding tables between the prefix and the suffix. In otherwords, the three-dimensional data decoding device arithmetic decodeseach of bits included in the prefix using coding tables for the prefix,and arithmetic decodes each of bits included in the suffix using codingtables for the suffix.

It is to be noted that the three-dimensional data decoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred at the time of decoding.In addition, the three-dimensional data decoding device may fix theoccurrence probabilities of 0 and 1 in one of coding tables. In thisway, it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount. For example,the three-dimensional data decoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

Furthermore, the three-dimensional data decoding device decodes thequantized prediction residual (unsigned integer value) by debinarizingthe binary data of the prediction residual arithmetic decoded accordingto a method in conformity with the encoding method used by thethree-dimensional data encoding device. The three-dimensional datadecoding device first arithmetic decodes the binary data of an n-bitcode to calculate a value of the n-bit code. Next, the three-dimensionaldata decoding device compares the value of the n-bit code with thresholdvalue R_TH.

In the case where the value of the n-bit code and threshold value R_THmatch, the three-dimensional data decoding device determines that a bitencoded using exponential-Golomb coding is present next, and arithmeticdecodes the remaining code which is the binary data encoded usingexponential-Golomb coding. The three-dimensional data decoding devicethen calculates, from the decoded remaining code, a value of theremaining code using a reverse lookup table indicating the relationshipbetween the remaining code and the value. FIG. 62 is a diagramindicating an example of a reverse lookup table indicating relationshipsbetween remaining codes and the values thereof. Next, thethree-dimensional data decoding device adds the obtained value of theremaining code to R_TH, thereby obtaining a debinarized quantizedprediction residual.

In the opposite case where the value of the n-bit code and thresholdvalue R_TH do not match (the value of the n-bit code is smaller thanvalue R_TH), the three-dimensional data decoding device determines thevalue of the n-bit code to be the debinarized quantized predictionresidual as it is. In this way, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream generatedwhile switching the binarization methods according to the values of theprediction residuals by the three-dimensional data encoding device.

It is to be noted that, when threshold value R_TH is added to, forexample, a header of a bitstream, the three-dimensional data decodingdevice may decode threshold value R_TH from the header, and may switchdecoding methods using decoded threshold value R_TH. When thresholdvalue R_TH is added to, for example, a header for each LoD, thethree-dimensional data decoding device switch decoding methods usingthreshold value R_TH decoded for each LoD.

For example, when threshold value R_TH is 63 and the value of thedecoded n-bit code is 63, the three-dimensional data decoding devicedecodes the remaining code using exponential-Golomb coding, therebyobtaining the value of the remaining code. For example, in the exampleindicated in FIG. 62, the remaining code is 00100, and 3 is obtained asthe value of the remaining code. Next, the three-dimensional datadecoding device adds 63 that is threshold value R_TH and 3 that is thevalue of the remaining code, thereby obtaining 66 that is the value ofthe prediction residual.

In addition, when the value of the decoded n-bit code is 32, thethree-dimensional data decoding device sets 32 that is the value of then-bit code to the value of the prediction residual.

In addition, the three-dimensional data decoding device converts thedecoded quantized prediction residual, for example, from an unsignedinteger value to a signed integer value, through processing inverse tothe processing in the three-dimensional data encoding device. In thisway, when entropy decoding the prediction residual, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream generated without considering occurrence of anegative integer. It is to be noted that the three-dimensional datadecoding device does not always need to convert an unsigned integervalue to a signed integer value, and that, for example, thethree-dimensional data decoding device may decode a sign bit whendecoding a bitstream generated by separately entropy encoding the signbit.

The three-dimensional data decoding device performs decoding by inversequantizing and reconstructing the quantized prediction residual afterbeing converted to the signed integer value, to obtain a decoded value.The three-dimensional data decoding device uses the generated decodedvalue for prediction of a current three-dimensional point to be decodedand the following three-dimensional point(s). More specifically, thethree-dimensional data decoding device multiplies the quantizedprediction residual by a decoded quantization scale to calculate aninverse quantized value and adds the inverse quantized value and thepredicted value to obtain the decoded value.

The decoded unsigned integer value (unsigned quantized value) isconverted into a signed integer value through the processing indicatedbelow. When the least significant bit (LSB) of decoded unsigned integervalue a2 u is 1, the three-dimensional data decoding device sets signedinteger value a2 q to −((a2 u+1). When the LSB of unsigned integer valuea2 u is not 1, the three-dimensional data decoding device sets signedinteger value a2 q to ((a2 u>>1).

Likewise, when an LSB of decoded unsigned integer value b2 u is 1, thethree-dimensional data decoding device sets signed integer value b2 q to−((b2 u+1.)>>1.). When the LSB of decoded unsigned integer value n2 u isnot 1, the three-dimensional data decoding device sets signed integervalue b2 q to ((b2 u>>1).

Details of the inverse quantization and reconstruction processing by thethree-dimensional data decoding device are similar to the inversequantization and reconstruction processing in the three-dimensional dataencoding device.

Hereinafter, a description is given of a flow of processing in thethree-dimensional data decoding device. FIG. 63 is a flowchart of athree-dimensional data decoding process performed by thethree-dimensional data decoding device. First, the three-dimensionaldata decoding device decodes geometry information (geometry) from abitstream (S3031). For example, the three-dimensional data decodingdevice performs decoding using octree representation.

Next, the three-dimensional data decoding device decodes attributeinformation (attribute) from the bitstream (S3032). For example, whendecoding a plurality of kinds of attribute information, thethree-dimensional data decoding device may decode the plurality of kindsof attribute information in order. For example, when decoding colors andreflectances as attribute information, the three-dimensional datadecoding device decodes the color encoding results and the reflectanceencoding results in order of assignment in the bitstream. For example,when the reflectance encoding results are added after the color encodingresults in a bitstream, the three-dimensional data decoding devicedecodes the color encoding results, and then decodes the reflectanceencoding results. It is to be noted that the three-dimensional datadecoding device may decode, in any order, the encoding results of theattribute information added to the bitstream.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device. Inaddition, the three-dimensional data decoding device may decode aplurality of kinds of attribute information in parallel, and mayintegrate the decoding results into a single three-dimensional pointcloud. In this way, the three-dimensional data decoding device iscapable of decoding the plurality of kinds of attribute information athigh speed;

FIG. 64 is a flowchart of an attribute information decoding process(S3032). First, the three-dimensional data decoding device sets LoDs(S3041). In other words, the three-dimensional data decoding deviceassigns each of three-dimensional points having the decoded geometryinformation to any one of the plurality of LoDs. For example, thisassignment method is the same as the assignment method used in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device starts a loop for eachLoD (S3042). In other words, the three-dimensional data decoding deviceiteratively performs the processes of Steps from S3043 to S3049 for eachLoD.

Next, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3043). In other words, the three-dimensionaldata decoding device iteratively performs the processes of Steps fromS3044 to S3048 for each three-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point to be processed (S3044). Next, thethree-dimensional data decoding device calculates the weighted averageof the values of attribute information of the plurality of neighborpoints, and sets the resulting value to predicted value P (S3045). It isto be noted that these processes are similar to the processes in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device arithmetic decodes thequantized value from the bitstream (S3046). The three-dimensional datadecoding device inverse quantizes the decoded quantized value tocalculate an inverse quantized value (S3047). Next, thethree-dimensional data decoding device adds a predicted value to theinverse quantized value to generate a decoded value (S3048). Next, thethree-dimensional data decoding device ends the loop for eachthree-dimensional point (S3049). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3050).

The following describes configurations of the three-dimensional dataencoding device and three-dimensional data decoding device according tothe present embodiment. FIG. 65 is a block diagram illustrating aconfiguration of three-dimensional data encoding device 3000 accordingto the present embodiment. Three-dimensional data encoding device 3000includes geometry information encoder 3001, attribute informationre-assigner 3002, and attribute information encoder 3003.

Attribute information encoder 3003 encodes geometry information(geometry) of a plurality of three-dimensional points included in aninput point cloud. Attribute information re-assigner 3002 re-assigns thevalues of attribute information of the plurality of three-dimensionalpoints included in the input point cloud, using the encoding anddecoding results of the geometry information. Attribute informationencoder 3003 encodes the re-assigned attribute information (attribute).Furthermore, three-dimensional data encoding device 3000 generates abitstream including the encoded geometry information and the encodedattribute information;

FIG. 66 is a block diagram illustrating a configuration ofthree-dimensional data decoding device 3010 according to the presentembodiment. Three-dimensional data decoding device 3010 includesgeometry information decoder 3011 and attribute information decoder3012. Geometry information decoder 3011 decodes the geometry information(geometry) of a plurality of three-dimensional points from a bitstream.Attribute information decoder 3012 decodes the attribute information(attribute) of the plurality of three-dimensional points from thebitstream. Furthermore, three-dimensional data decoding device 3010integrates the decoded geometry information and the decoded attributeinformation to generate an output point cloud.

As described above, the three-dimensional data encoding device accordingto the present embodiment performs the process illustrated in FIG. 67.The three-dimensional data encoding device encodes a three-dimensionalpoint having attribute information. First, the three-dimensional dataencoding device calculates a predicted value of the attributeinformation of the three-dimensional point (S3061). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of thethree-dimensional point and the predicted value (S3062). Next, thethree-dimensional data encoding device binarizes the prediction residualto generate binary data (S3063). Next, the three-dimensional dataencoding device arithmetic encodes the binary data (S3064).

In this way, the three-dimensional data encoding device is capable ofreducing the code amount of the to-be-coded data of the attributeinformation by calculating the prediction residual of the attributeinformation, and binarizing and arithmetic encoding the predictionresidual.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device uses coding tables different for each of bits of binarydata. By doing so, the three-dimensional data encoding device canincrease the coding efficiency.

For example, in arithmetic encoding (S3064), the number of coding tablesto be used is larger for a lower-order bit of the binary data.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device selects coding tables to be used to arithmetic encode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. By doing so, since thethree-dimensional data encoding device can select coding tables to beused according to the value of the higher-order bit, thethree-dimensional data encoding device can increase the codingefficiency.

For example, in binarization (S3063), the three-dimensional dataencoding device: binarizes a prediction residual using a fixed bit countto generate binary data when the prediction residual is smaller than athreshold value (R_TH); and generates binary data including a first code(n-bit code) and a second code (remaining code) when the predictionresidual is larger than or equal to the threshold value (R_TH). Thefirst code is of a fixed bit count indicating the threshold value(R_TH), and the second code (remaining code) is obtained by binarizing,using exponential-Golomb coding, the value obtained by subtracting thethreshold value (R_TH) from the prediction residual. In arithmeticencoding (S3064), the three-dimensional data encoding device usesarithmetic encoding methods different between the first code and thesecond code.

With this, for example, since it is possible to arithmetic encode thefirst code and the second code using arithmetic encoding methodsrespectively suitable for the first code and the second code, it ispossible to increase coding efficiency.

For example, the three-dimensional data encoding device quantizes theprediction residual, and, in binarization (S3063), binarizes thequantized prediction residual. The threshold value (R_TH) is changedaccording to a quantization scale in quantization. With this, since thethree-dimensional data encoding device can use the threshold valuesuitably according to the quantization scale, it is possible to increasethe coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic encoding (S3064), the three-dimensional data encoding deviceuses different coding tables between the prefix and the suffix. In thisway, the three-dimensional data encoding device can increase the codingefficiency.

For example, the three-dimensional data encoding device includes aprocessor and memory and the processor performs the above process usingthe memory.

The three-dimensional data decoding device according to the presentembodiment performs the process illustrated in FIG. 68. Thethree-dimensional data decoding device decodes a three-dimensional pointhaving attribute information. First, the three-dimensional data decodingdevice calculates a predicted value of the attribute information of athree-dimensional point (S3071). Next, the three-dimensional datadecoding device arithmetic decodes encoded data included in a bitstreamto generate binary data (S3072). Next, the three-dimensional datadecoding device debinarizes the binary data to generate a predictionresidual (S3073). Next, the three-dimensional data decoding devicecalculates a decoded value of the attribute information of thethree-dimensional point by adding the predicted value and the predictionresidual (S3074).

In this way, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream of the attribute informationgenerated by calculating the prediction residual of the attributeinformation and binarizing and arithmetic decoding the predictionresidual.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device uses coding tables different for each of bits of binarydata. With this, the three-dimensional data decoding device is capableof appropriately decoding the bitstream encoded at an increased codingefficiency.

For example, in arithmetic decoding (S3072), the number of coding tablesto be used is larger for a lower bit of the binary data.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device selects coding tables to be used to arithmetic decode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. With this, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at an increased coding efficiency.

For example, in debinarization (S3073), the three-dimensional datadecoding device debinarizes the first code (n-bit code) of a fixed bitcount included in the binary data to generate a first value. Thethree-dimensional data decoding device: determines the first value to bethe prediction residual when the first value is smaller than thethreshold value (R_TH); and, when the first value is larger than orequal to the threshold value (R_YH), generates a second value bydebinarizing the second code (remaining code) which is anexponential-Golomb code included in the binary data and adds the firstvalue and the second value, thereby generating a prediction residual. Inthe arithmetic decoding (S3072), the three-dimensional data decodingdevice uses arithmetic decoding methods different between the first codeand the second code.

With this, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three dimensional data decoding device inversequantizes the prediction residual, and, in addition (S3074), adds thepredicted value and the inverse quantized prediction residual. Thethreshold value (R_TH) is changed according to a quantization scale ininverse quantization. With this, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream encoded at anincreased coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic decoding (S3072), the three-dimensional data decoding deviceuses different coding tables between the prefix and the suffix. Withthis, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three-dimensional data decoding device includes aprocessor and memory and the processor performs the above-describedprocess using the memory.

Embodiment 9

Predicted values may be generated by a method different from that inEmbodiment 8. Hereinafter, a three-dimensional point to be encoded isreferred to as a first three-dimensional point, and one or morethree-dimensional points in the vicinity of the first three-dimensionalpoint is referred to as one or more second three-dimensional points insome cases.

For example, in generating of a predicted value of an attributeinformation item (attribute information) of a three-dimensional point,an attribute value as it is of a closest three-dimensional point amongencoded and decoded three-dimensional points in the vicinity of athree-dimensional point to be encoded may be generated as a predictedvalue. In the generating of the predicted value, prediction modeinformation (PredMode) may be appended for each three-dimensional point,and one predicted value may be selected from a plurality of predictedvalues to allow generation of a predicted value. Specifically, forexample, it is conceivable that, for total number M of prediction modes,an average value is assigned to prediction mode 0, an attribute value ofthree-dimensional point A is assigned to prediction mode 1, . . . , andan attribute value of three-dimensional point Z is assigned toprediction mode M-1, and the prediction mode used for prediction isappended to a bitstream for each three-dimensional point. As such, afirst prediction mode value indicating a first prediction mode forcalculating, as a predicted value, an average of attribute informationitems of the surrounding three-dimensional points may be smaller than asecond prediction mode value indicating second prediction mode forcalculating, as a predicted value, an attribute information item as itis of a surrounding three-dimensional point. Here, the “average value”as the predicted value calculated in prediction mode 0 is an averagevalue of the attribute values of the three-dimensional points in thevicinity of the three-dimensional point to be encoded;

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. FIG. 70 is a diagram showing examples of attributeinformation items used as the predicted values according to the presentembodiment. FIG. 71 is a diagram showing a second example of a tablerepresenting predicted values calculated in the prediction modesaccording to the present embodiment.

Number M of prediction modes may be appended to a bitstream. Number M ofprediction modes may be defined by a profile or a level of standardsrather than appended to the bitstream. Number M of prediction modes maybe also calculated from number N of three-dimensional points used forprediction. For example, number M of prediction modes may be calculatedby M=N+1.

The table in FIG. 69 is an example of a case with number N ofthree-dimensional points used for prediction being 4 and number M ofprediction modes being 5. A predicted value of an attribute informationitem of point b2 can be generated by using attribute information itemsof points a0, a1, a2, b1. In selecting one prediction mode from aplurality of prediction modes, a prediction mode for generating, as apredicted value, an attribute value of each of points a0, a1, a2, b1 maybe selected in accordance with distance information from point b2 toeach of points a0, a1, a2, b1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

The table in FIG. 71 is, as in FIG. 69, an example of a case with numberN of three-dimensional points used for prediction being 4 and number Mof prediction modes being 5. A predicted value of an attributeinformation item of point a2 can be generated by using attributeinformation items of points a0, a1. In selecting one prediction modefrom a plurality of prediction modes, a prediction mode for generating,as a predicted value, an attribute value of each of points a0 and a1 maybe selected in accordance with distance information from point a2 toeach of points a0, a1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

When the number of neighboring points, that is, number N of surroundingthree-dimensional points is smaller than four such as at point a2 above,a prediction mode to which a predicted value is not assigned may bewritten as “not available” in the table.

Assignment of values of the prediction modes may be determined inaccordance with the distance from the three-dimensional point to beencoded. For example, prediction mode values indicating a plurality ofprediction modes decrease with decreasing distance from thethree-dimensional point to be encoded to the surroundingthree-dimensional points having the attribute information items used asthe predicted values. The example in FIG. 69 shows that points b1, a2,a1, a0 are sequentially located closer to point b2 as thethree-dimensional point to be encoded. For example, in the calculatingof the predicted value, the attribute information item of point b1 iscalculated as the predicted value in a prediction mode indicated by aprediction mode value of “1” among two or more prediction modes, and theattribute information item of point a2 is calculated as the predictedvalue in a prediction mode indicated by a prediction mode value of “2”.As such, the prediction mode value indicating the prediction mode forcalculating, as the predicted value, the attribute information item ofpoint b1 is smaller than the prediction mode value indicating theprediction mode for calculating, as the predicted value, the attributeinformation item of point a2 farther from point b2 than point b1.

Thus, a small prediction mode value can be assigned to a point that ismore likely to be predicted and selected due to a short distance,thereby reducing a bit number for encoding the prediction mode value.Also, a small prediction mode value may be preferentially assigned to athree-dimensional point belonging to the same LoD as thethree-dimensional point to be encoded;

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. Specifically, the third example is an example of acase where an attribute information item used as a predicted value is avalue of color information (YUV) of a surrounding three-dimensionalpoint. As such, the attribute information item used as the predictedvalue may be color information indicating a color of thethree-dimensional point.

As shown in FIG. 72, a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is an average of Y, U, and Vcomponents defining a YUV color space. Specifically, the predicted valueincludes a weighted average Yave of Y component values Yb1, Ya2, Ya1,Ya0 corresponding to points b1, a2, a1, a0, respectively a weightedaverage Uave of U component values Ub1, Ua2, Ua1, Ua0 corresponding topoints b1, a2, a1, a0, respectively, and a weighted average Vave of Vcomponent values Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1,a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” include colorinformation of the surrounding three-dimensional points b1, a2, a1, a0.The color information is indicated by combinations of the Y, U, and Vcomponent values.

In FIG. 72, the color information is indicated by a value defined by theYUV color space, but not limited to the YUV color space. The colorinformation may be indicated by a value defined by an RGB color space ora value defined by any other color space.

As such, in the calculating of the predicted value, two or more averagesor two or more attribute information items may be calculated as thepredicted values of the prediction modes. The two or more averages orthe two or more attribute information items may indicate two or morecomponent values each defining a color space.

For example, when a prediction mode indicated by a prediction mode valueof “2” in the table in FIG. 72 is selected, a Y component, a Ucomponent, and a V component as attribute values of thethree-dimensional point to be encoded may be encoded as predicted valuesYa2, Ua2, Va2. In this case, the prediction mode value of “2” isappended to the bitstream;

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. Specifically, the fourth example is an example of acase where an attribute information item used as a predicted value is avalue of reflectance information of a surrounding three-dimensionalpoint. The reflectance information is, for example, informationindicating reflectance R.

As shown in FIG. 73, a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is weighted average Rave ofreflectances Rb1, Ra2, Ra1, Ra0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4” are reflectances Rb1, Ra2, Ra1,Ra0 of surrounding three-dimensional points b1, a2, a1, a0, respectively

For example, when a prediction mode indicated by a prediction mode valueof “3” in the table in FIG. 73 is selected, a reflectance as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ra1. In this case, the prediction mode valueof “3” is appended to the bitstream.

As shown in FIGS. 72 and 73, the attribute information item may includea first attribute information item and a second attribute informationitem different from the first attribute information item. The firstattribute information item is, for example, color information. Thesecond attribute information item is, for example, reflectanceinformation. In the calculating of the predicted value, a firstpredicted value may be calculated by using the first attributeinformation item, and a second predicted value may be calculated byusing the second attribute information item.

Embodiment 10

Hereinafter, a method using a Region Adaptive Hierarchical Transform(RAHT) will be described as another method of encoding the attributeinformation of a three-dimensional point. FIG. 74 is a diagram fordescribing the encoding of the attribute information by using a RAHT.

First, the three-dimensional data encoding device generates Morton codesbased on the geometry information of three-dimensional points, and sortsthe attribute information of the three-dimensional points in the orderof the Morton codes. For example, the three-dimensional data encodingdevice may perform sorting in the ascending order of the Morton codes.Note that the sorting order is not limited to the order of the Mortoncodes, and other orders may be used.

Next, the three-dimensional data encoding device generates ahigh-frequency component and a low-frequency component of the layer L byapplying the Haar conversion to the attribute information of twoadjacent three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional data encoding device may use the Haarconversion of 2×2 matrices. The generated high-frequency component isincluded in a coding coefficient as the high-frequency component of thelayer L, and the generated low-frequency component is used as the inputvalue for the higher layer L+1 of the layer L.

After generating the high-frequency component of the layer L by usingthe attribute information of the layer L, the three-dimensional dataencoding device subsequently performs processing of the layer L+1. Inthe processing of the layer L+1, the three-dimensional data encodingdevice generates a high-frequency component and a low-frequencycomponent of the layer L+1 by applying the Haar conversion to twolow-frequency components obtained by the Haar conversion of theattribute information of the layer L. The generated high-frequencycomponent is included in a coding coefficient as the high-frequencycomponent of the layer L+1, and the generated low-frequency component isused as the input value for the higher layer L+2 of the layer L+1.

The three-dimensional data encoding device repeats such layerprocessing, and determines that the highest layer Lmax has been reachedat the time when a low-frequency component that is input to a layerbecomes one. The three-dimensional data encoding device includes thelow-frequency component of the layer Lmax-1 that is input to the LayerLmax in a coding coefficient. Then, the value of the low-frequencycomponent or high-frequency component included in the coding coefficientis quantized, and is encoded by using entropy encoding or the like.

Note that, when only one three-dimensional point exists as two adjacentthree-dimensional points at the time of application of the Haarconversion, the three-dimensional data encoding device may use the valueof the attribute information of the existing one three-dimensional pointas the input value for a higher layer.

In this manner, the three-dimensional data encoding devicehierarchically applies the Haar conversion to the input attributeinformation, generates a high-frequency component and a low-frequencycomponent of the attribute information, and performs encoding byapplying quantization described later or the like. Accordingly thecoding efficiency can be improved.

When the attribute information is N dimensional, the three-dimensionaldata encoding device may independently apply the Haar conversion foreach dimension, and may calculate each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data encoding device applies the Haarconversion for each component, and calculates each coding coefficient.

The three-dimensional data encoding device may apply the Haar conversionin the order of the layers L, L+1, . . . , Lmax. The closer to the layerLmax, a coding coefficient including the more low-frequency componentsof the input attribute information is generated.

w0 and w1 shown in FIG. 74 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional dataencoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theHaar conversion is applied, or the like. For example, thethree-dimensional data encoding device may improve the coding efficiencysuch that the closer the distance, the greater the weight. Note that thethree-dimensional data encoding device may calculate this weight withanother technique, or need not use the weight.

In the example shown in FIG. 74, the pieces of the input attributeinformation are a0, a1, a2, a3, a4, and a5. Additionally, Ta1, Ta5, Tb1,Tb3, Tc1, and d0 are encoded among the coding coefficients after theHaar conversion. The other coding coefficients (b0, b2, c0 and the like)are medians, and are not encoded.

Specifically, in the example shown in FIG. 74, the high-frequencycomponent Ta1 and the low-frequency component b0 are generated byperforming the Haar conversion on a0 and a1. Here, when the weights w0and w1 are equal, the low-frequency component b0 is the average value ofa0 and a1, and the high-frequency component Ta1 is the differencebetween a0 and a1.

Since there is no attribute information to be paired with a2, a2 is usedas b1 as is. Similarly, since there is no attribute information to bepaired with a3, a3 is used as b2 as is. Additionally, the high-frequencycomponent Ta5 and the low-frequency component b3 are generated byperforming the Haar conversion on a4 and a5.

In the layer L+1, the high-frequency component Tb1 and the low-frequencycomponent c0 are generated by performing the Haar conversion on b0 andb1. Similarly, the high-frequency component Tb3 and the low-frequencycomponent c1 are generated by performing the Haar conversion on b2 andb3.

In the layer Lmax-1, the High-frequency component Tc1 and thelow-frequency component d0 are generated by performing the Haarconversion on c0 and c1.

The three-dimensional data encoding device may encode the codingcoefficients to which the Haar conversion has been applied, afterquantizing the coding coefficients. For example, the three-dimensionaldata encoding device performs quantization by dividing the codingcoefficient by the quantization scale (also called the quantization step(QS)). In this case, the smaller the quantization scale, the smaller theerror (quantization error) that may occur due to quantization.Conversely, the larger the quantization scale, the larger thequantization error.

Note that the three-dimensional data encoding device may change thevalue of the quantization scale for each layer. FIG. 75 is a diagramshowing an example of setting the quantization scale for each layer. Forexample, the three-dimensional data encoding device sets smallerquantization scales to the higher layers, and larger quantization scalesto the lower layers. Since the coding coefficients of thethree-dimensional points belonging to the higher layers include morelow-frequency components than the lower layers, there is a highpossibility that the coding coefficients are important components inhuman visual characteristics and the like. Therefore, by suppressing thequantization error that may occur in the higher layers by making thequantization scales for the higher layers small, visual deteriorationcan be suppressed, and the coding efficiency can be improved.

Note that the three-dimensional data encoding device may add thequantization scale for each layer to a header or the like. Accordingly,the three-dimensional decoding device can correctly decode thequantization scale, and can appropriately decode a bitstream.

Additionally, the three-dimensional data encoding device may adaptivelyswitch the value of the quantization scale according to the importanceof a current three-dimensional point to be encoded. For example, thethree-dimensional data encoding device uses a small quantization scalefor a three-dimensional point with high importance, and uses a largequantization scale for a three-dimensional point with low importance.For example, the three-dimensional data encoding device may calculatethe importance from the weight at the time of the Haar conversion, orthe like. For example, the three-dimensional data encoding device maycalculate the quantization scale by using the sum of w0 and w1. In thismanner, by making the quantization scale of a three-dimensional pointwith high importance small, the quantization error becomes small, andthe coding efficiency can be improved.

Additionally, the value of the QS may be made smaller for the higherlayers. Accordingly, the higher the layer, the larger the value of theQW, and the prediction efficiency can be improved by suppressing thequantization error of the three-dimensional point.

Here, a coding coefficient Ta1 q after quantization of the codingcoefficient Ta1 of the attribute information a1 is represented byTa1/QS_L. Note that QS may be the same value in all the layers or a partof the layers.

The QW (Quantization Weight) is the value that represents the importanceof a current three-dimensional point to be encoded. For example, theabove-described sum of w0 and w1 may be used as the QW. Accordingly, thehigher the layer, the larger the value of the QW, and the predictionefficiency can be improved by suppressing the quantization error of thethree-dimensional point.

For example, the three-dimensional data encoding device may firstinitialize the values of the QWs of all the three-dimensional pointswith 1, and may update the QW of each three-dimensional point by usingthe values of w0 and w1 at the time of the Haar conversion.Alternatively, the three-dimensional data encoding device may change theinitial value according to the layers, without initializing the valuesof the QWs of all the three-dimensional points with a value of 1. Forexample, the quantization scales for the higher layers becomes small bysetting larger QW initial values for the higher layers. Accordingly,since the prediction error in the higher layers can be suppressed, theprediction accuracy of the lower layers can be increased, and the codingefficiency can be improved. Note that the three-dimensional dataencoding device need not necessarily use the QW.

When using the QW, the quantized value Ta1 q of Ta1 is calculated by(Equation K1) and (Equation K2).

$\begin{matrix}{{{Ta}\; 1\; q} = {\frac{{{Ta}\; 1} + \frac{QS\_ L}{2}}{{QS\_ LoD}\; 1} \times {QWTa}\; 1}} & \left( {{Equation}\mspace{14mu} K\; 1} \right) \\{{{QWTa}\; 1} = {1 + {\sum\limits_{i = 0}^{1}w_{i}}}} & \left( {{Equation}\mspace{14mu} K\; 2} \right)\end{matrix}$

Additionally, the three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) afterquantization in a certain order. For example, the three-dimensional dataencoding device encodes a plurality of three-dimensional points from thethree-dimensional points included in the higher layers toward the lowerlayers in order.

For example, in the example shown in FIG. 74, the three-dimensional dataencoding device encodes a plurality of three-dimensional points in theorder of Tc1 q Tb1 q, Tb3 q, Ta1 q, and Ta5 q from d0 q included in thehigher layer Lmax. Here, there is a tendency that the lower the layer L,the more likely it is that the coding coefficient after quantizationbecomes 0. This can be due to the following and the like.

Since the coding coefficient of the lower layer L shows a higherfrequency component than the higher layers, there is a tendency that thecoding coefficient becomes 0 depending on a current three-dimensionalpoint. Additionally, by switching the quantization scale according tothe above-described importance or the like, the lower the layer, thelarger the quantization scales, and the more likely it is that thecoding coefficient after quantization becomes 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after quantization becomes 0, and the value 0consecutively occurs in the first code sequence. FIG. 76 is a diagramshowing an example of the first code sequence and the second codesequence.

The three-dimensional data encoding device counts the number of timesthat the value 0 occurs in the first code sequence, and encodes thenumber of times that the value 0 consecutively occurs, instead of theconsecutive values 0. That is, the three-dimensional data encodingdevice generates a second code sequence by replacing the codingcoefficient of the consecutive values 0 in the first code sequence withthe number of consecutive times (ZeroCnt) of 0. Accordingly when thereare consecutive values 0 of the coding coefficients after quantization,the coding efficiency can be improved by encoding the number ofconsecutive times of 0, rather than encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may entropyencode the value of ZeroCnt. For example, the three-dimensional dataencoding device binarizes the value of ZeroCnt with the truncated unarycode of the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after the binarization. FIG. 77 is adiagram showing an example of the truncated unary code in the case wherethe total number of encoded three-dimensional points is T. At this time,the three-dimensional data encoding device may improve the codingefficiency by using a different coding table for each bit. For example,the three-dimensional data encoding device uses coding table 1 for thefirst bit, uses coding table 2 for the second bit, and coding table 3for the subsequent bits. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by switching thecoding table for each bit.

Additionally the three-dimensional data encoding device mayarithmetically encode ZeroCnt after binarizing ZeroCnt with anExponential-Golomb. Accordingly when the value of ZeroCnt easily becomeslarge, the efficiency can be more improved than the binarized arithmeticencoding with the truncated unary code. Note that the three-dimensionaldata encoding device may add a flag for switching between using thetruncated unary code and using the Exponential-Golomb to a header.Accordingly the three-dimensional data encoding device can improve thecoding efficiency by selecting the optimum binarization method.Additionally, the three-dimensional data decoding device can correctlydecode a bitstream by referring to the flag included in the header toswitch the binarization method.

The three-dimensional decoding device may convert the decoded codingcoefficient after the quantization from an unsigned integer value to asigned integer value with a method contrary to the method performed bythe three-dimensional data encoding device. Accordingly, when the codingcoefficient is entropy encoded, the three-dimensional decoding devicecan appropriately decode a bitstream generated without considering theoccurrence of a negative integer. Note that the three-dimensionaldecoding device does not necessarily need to convert the codingcoefficient from an unsigned integer value to a signed integer value.For example, when decoding a bitstream including an encoded bit that hasbeen separately entropy encoded, the three-dimensional decoding devicemay decode the sign bit.

The three-dimensional decoding device decodes the coding coefficientafter the quantization converted to the signed integer value, by theinverse quantization and the inverse Haar conversion. Additionally, thethree-dimensional decoding device utilizes the coding coefficient afterthe decoding for the prediction after the current three-dimensionalpoint to be decoded. Specifically, the three-dimensional decoding devicecalculates the inverse quantized value by multiplying the codingcoefficient after the quantization by the decoded quantization scale.Next, the three-dimensional decoding device obtains the decoded value byapplying the inverse Haar conversion described later to the inversequantized value.

For example, the three-dimensional decoding device converts the decodedunsigned integer value to a signed integer value with the followingmethod. When the LSB (least significant bit) of the decoded unsignedinteger value a2 u is 1, the signed integer value Ta1 q is set to −((a2u+1)>>1). When the LSB of the decoded unsigned integer value a2 u is not1 (when it is 0), the signed integer value Ta1 q is set to (a2 u>>1).

Additionally, the inverse quantized value of Ta1 is represented by Ta1q×QS_L. Here, Ta1 q is the quantized value of Ta1. In addition, QS_L isthe quantization step for the layer L.

Additionally, the QS may be the same value for all the layers or a partof the layers. In addition, the three-dimensional data encoding devicemay add the information indicating the QS to a header or the like.Accordingly, the three-dimensional decoding device can correctly performinverse quantization by using the same QS as the QS used by thethree-dimensional data encoding device.

Next, the inverse Haar conversion will be described. FIG. 78 is adiagram for describing the inverse Haar conversion. Thethree-dimensional decoding device decodes the attribute value of athree-dimensional point by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization.

First, the three-dimensional decoding device generates the Morton codesbased on the geometry information of three-dimensional points, and sortsthe three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional decoding device may perform the sortingin ascending order of the Morton codes. Note that the sorting order isnot limited to the order of the Morton codes, and the other order may beused.

Next, the three-dimensional decoding device restores the attributeinformation of three-dimensional points that are adjacent to each otherin the order of the Morton codes in the layer L, by applying the inverseHaar conversion to the coding coefficient including the low-frequencycomponent of the layer L+1, and the coding coefficient including thehigh-frequency component of the layer L. For example, thethree-dimensional decoding device may use the inverse Haar conversion ofa 2×2 matrix. The attribute information of the restored layer L is usedas the input value for the lower layer L−1.

The three-dimensional decoding device repeats such layer processing, andends the processing when all the attribute information of the bottomlayer is decoded. Note that, when only one three-dimensional pointexists as two three-dimensional points that are adjacent to each otherin the layer L−1 at the time of application of the inverse Haarconversion, the three-dimensional decoding device may assign the valueof the encoding component of the layer L to the attribute value of theone existing three-dimensional point. Accordingly, the three-dimensionaldecoding device can correctly decode a bitstream with improved codingefficiency by applying the Haar conversion to all the values of theinput attribute information.

When the attribute information is N dimensional, the three-dimensionaldecoding device may independently apply the inverse Haar conversion foreach dimension, and may decode each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data decoding device applies the inverseHaar conversion to the coding coefficient for each component, anddecodes each attribute value.

The three-dimensional decoding device may apply the inverse Haarconversion in the order of Lasers Lmax, L+1, . . . , L. Additionally, w0and w1 shown in FIG. 78 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional datadecoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theinverse Haar conversion is applied, or the like. For example, thethree-dimensional data encoding device may decode a bitstream withimproved coding efficiency such that the closer the distance, thegreater the weight.

In the example shown in FIG. 78, the coding coefficients after theinverse quantization are Ta1, Ta5, Tb1, Tb3, Tc1, and d0, and a0, a1,a2, a3, a4, and a5 are obtained as the decoded values.

FIG. 79 is a diagram showing a syntax example of the attributeinformation (attribute_data). The attribute information (attribute_data)includes the number of consecutive zeros (ZeroCnt), the number ofattribute dimensions (attribute_dimension), and the coding coefficient(value [j] [i]).

The number of consecutive zeros (ZeroCnt) indicates the number of timesthat the value 0 continues in the coding coefficient after quantization.Note that the three-dimensional data encoding device may arithmeticallyencode ZeroCnt after binarizing ZeroCnt.

Additionally, as shown in FIG. 79, the three-dimensional data encodingdevice may determine whether or not the layer L (layerL) to which thecoding coefficient belongs is equal to or more than a predefinedthreshold value TH_layer, and may switch the information added to abitstream according to the determination result. For example, when thedetermination result is true, the three-dimensional data encoding deviceadds all the coding coefficients of the attribute information to abitstream. In addition, when the determination result is false, thethree-dimensional data encoding device may add a part of the codingcoefficients to a bitstream.

Specifically, when the determination result is true, thethree-dimensional data encoding device adds the encoded result of thethree-dimensional information of the color information RGB or YUV to abitstream. When the determination result is false, the three-dimensionaldata encoding device may add a part of information such as G or Y of thecolor information to a bitstream, and need not to add the othercomponents to the bitstream. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by not adding a partof the coding coefficients of the layer (the layer smaller thanTH_layer) including the coding coefficients indicating thehigh-frequency component with less visually noticeable degradation to abitstream.

The number of attribute dimensions (attribute_dimension) indicates thenumber of dimensions of the attribute information. For example, when theattribute information is the color information (RGB, YUV, or the like)of a three-dimensional point, since the color information isthree-dimensional, the number of attribute dimensions is set to a value3. When the attribute information is the reflectance, since thereflectance is one-dimensional, the number of attribute dimensions isset to a value 1. Note that the number of attribute dimensions may beadded to the header of the attribute information of a bit stream or thelike.

The coding coefficient (value [j] [i]) indicates the coding coefficientafter quantization of the attribute information of the j-th dimension ofthe i-th three-dimensional point. For example, when the attributeinformation is color information, value [99] [1] indicates the codingcoefficient of the second dimension (for example, the G value) of the100th three-dimensional point. Additionally, when the attributeinformation is reflectance information, value [119] [0] indicates thecoding coefficient of the first dimension (for example, the reflectance)of the 120th three-dimensional point.

Note that, when the following conditions are satisfied, thethree-dimensional data encoding device may subtract the value 1 fromvalue [j] [i], and may entropy encode the obtained value. In this case,the three-dimensional data decoding device restores the codingcoefficient by adding the value 1 to value [j] [i] after entropydecoding.

The above-described conditions are (1) when attribute_dimension=1, or(2) when attribute_dimension is 1 or more, and when the values of allthe dimensions are equal. For example, when the attribute information isthe reflectance, since attribute_dimension=1, the three-dimensional dataencoding device subtracts the value 1 from the coding coefficient tocalculate value, and encodes the calculated value. The three-dimensionaldecoding device calculates the coding coefficient by adding the value 1to the value after decoding.

More specifically, for example, when the coding coefficient of thereflectance is 10, the three-dimensional data encoding device encodesthe value 9 obtained by subtracting the value 1 from the value 10 of thecoding coefficient. The three-dimensional data decoding device adds thevalue 1 to the decoded value 9 to calculate the value 10 of the codingcoefficient.

Additionally, since attribute_dimension=3 when the attribute informationis the color, for example, when the coding coefficient afterquantization of each of the components R, G, and B is the same, thethree-dimensional data encoding device subtracts the value 1 from eachcoding coefficient, and encodes the obtained value. Thethree-dimensional data decoding device adds the value 1 to the valueafter decoding. More specifically, for example, when the codingcoefficient of R, G, and B=(1, 1, 1), the three-dimensional dataencoding device encodes (0, 0, 0). The three-dimensional data decodingdevice adds 1 to each component of (0, 0, 0) to calculate (1, 1, 1).Additionally, when the coding coefficients of R, G, and B=(2, 1, 2), thethree-dimensional data encoding device encodes (2, 1, 2) as is. Thethree-dimensional data decoding device uses the decoded (2, 1, 2) as isas the coding coefficients.

In this manner, by providing ZeroCnt, since the pattern in which all thedimensions are 0 as value is not generated, the value obtained bysubtracting 1 from the value indicated by value can be encoded.Therefore, the coding efficiency can be improved.

Additionally, value [0] [i] shown in FIG. 79 indicates the codingcoefficient after quantization of the attribute information of the firstdimension of the i-th three-dimensional point. As shown in FIG. 79, whenthe layer L (layerL) to which the coding coefficient belongs is smallerthan the threshold value TH_layer, the code amount may be reduced byadding the attribute information of the first dimension to a bitstream(not adding the attribute information of the second and followingdimensions to the bitstream).

The three-dimensional data encoding device may switch the calculationmethod of the value of ZeroCnt depending on the value ofattribute_dimension. For example, when attribute_dimension=3, thethree-dimensional data encoding device may count the number of timesthat the values of the coding coefficients of all the components(dimensions) become 0. FIG. 80 is a diagram showing an example of thecoding coefficient and ZeroCnt in this case. For example, in the case ofthe color information shown in FIG. 80, the three-dimensional dataencoding device counts the number of the consecutive coding coefficientshaving 0 for all of the R, G, and B components, and adds the countednumber to a bitstream as ZeroCnt. Accordingly, it becomes unnecessary toencode ZeroCnt for each component, and the overhead can be reduced.Therefore, the coding efficiency can be improved. Note that thethree-dimensional data encoding device may calculate ZeroCnt for eachdimension even when attribute_dimension is two or more, and may add thecalculated ZeroCnt to a bitstream.

FIG. 81 is a flowchart of the three-dimensional data encoding processingaccording to the present embodiment. First, the three-dimensional dataencoding device encodes geometry information (geometry) (S6601). Forexample, the three-dimensional data encoding device performs encoding byusing an octree representation.

Next, the three-dimensional data encoding device converts the attributeinformation (S6602). For example, after the encoding of the geometryinformation, when the position of a three-dimensional point is changeddue to quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information of the originalthree-dimensional point to the three-dimensional point after the change.Note that the three-dimensional data encoding device may interpolate thevalue of the attribute information according to the amount of change ofthe position to perform the reassignment. For example, thethree-dimensional data encoding device detects N three-dimensionalpoints before the change near the three dimensional position after thechange, performs the weighted averaging of the value of the attributeinformation of the N three-dimensional points based on the distance fromthe three-dimensional position after the change to each of the Nthree-dimensional points, and sets the obtained value as the value ofthe attribute information of the three-dimensional point after thechange. Additionally, when two or more three-dimensional points arechanged to the same three-dimensional position due to quantization orthe like, the three-dimensional data encoding device may assign theaverage value of the attribute information in the two or morethree-dimensional points before the change as the value of the attributeinformation after the change.

Next, the three-dimensional data encoding device encodes the attributeinformation (S6603). For example, when encoding a plurality of pieces ofattribute information, the three-dimensional data encoding device mayencode the plurality of pieces of attribute information in order. Forexample, when encoding the color and the reflectance as the attributeinformation, the three-dimensional data encoding device generates abitstream to which the encoding result of the reflectance is added afterthe encoding result of the color. Note that a plurality of encodingresults of the attribute information added to a bitstream may be in anyorder.

Additionally, the three-dimensional data encoding device may add theinformation indicating the start location of the encoded data of eachattribute information in a bitstream to a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount for thethree-dimensional data decoding device can be reduced. Additionally, thethree-dimensional data encoding device may encode a plurality of piecesof attribute information in parallel, and may integrate the encodingresults into one bitstream. Accordingly the three-dimensional dataencoding device can encode a plurality of pieces of attributeinformation at high speed.

FIG. 82 is a flowchart of the attribute information encoding processing(S6603). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by the Haar conversion(S6611). Next, the three-dimensional data encoding device appliesquantization to the coding coefficient (S6612). Next, thethree-dimensional data encoding device generates encoded attributeinformation (bitstream) by encoding the coding coefficient after thequantization (S6613).

Additionally, the three-dimensional data encoding device applies inversequantization to the coding coefficient after the quantization (S6614).Next, the three-dimensional decoding device decodes the attributeinformation by applying the inverse Haar conversion to the codingcoefficient after the inverse quantization (S6615). For example, thedecoded attribute information is referred to in the following encoding.

FIG. 83 is a flowchart of the coding coefficient encoding processing(S6613). First, the three-dimensional data encoding device converts acoding coefficient from a signed integer value to an unsigned integervalue (S6621). For example, the three-dimensional data encoding deviceconverts a signed integer value to an unsigned integer value as follows.When signed integer value Ta1 q is smaller than 0, the unsigned integervalue is set to −1−(2 ×Ta1 q). When the signed integer value Ta1 q isequal to or more than 0, the unsigned integer value is set to 2×Ta1 q.Note that, when the coding coefficient does not become a negative value,the three-dimensional data encoding device may encode the codingcoefficient as the unsigned integer value as is.

When not all coding coefficients have been processed (No in S6622), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (86623). When the valueof the coding coefficient to be processed is zero (Yes in S6623), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6624),and returns to step S6622.

When the value of the coding coefficient to be processed is not zero (Noin S6623), the three-dimensional data encoding device encodes ZeroCnt,and resets ZeroCnt to zero (S6625). Additionally the three-dimensionaldata encoding device arithmetically encodes the coding coefficient to beprocessed (S6626), and returns to step S6622. For example, thethree-dimensional data encoding device performs binary arithmeticencoding. In addition, the three-dimensional data encoding device maysubtract the value 1 from the coding coefficient, and may encode theobtained value.

Additionally the processing of steps S6623 to S6626 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6622), the three-dimensionaldata encoding device ends the processing.

FIG. 84 is a flowchart of the three-dimensional data decoding processingaccording to the present embodiment. First, the three-dimensionaldecoding device decodes geometry information (geometry) from a bitstream(S6631). For example, the three-dimensional data decoding deviceperforms decoding by using an octree representation.

Next, the three-dimensional decoding device decodes the attributeinformation from the bitstream (S6632). For example, when decoding aplurality of pieces of attribute information, the three-dimensionaldecoding device may decode the plurality of pieces of attributeinformation in order. For example, when decoding the color and thereflectance as the attribute information, the three-dimensional datadecoding device decodes the encoding result of the color and theencoding result of the reflectance according to the order in which. theyare added to the bitstream. For example, when the encoding result of thereflectance is added after the encoding result of the color in abitstream, the three-dimensional data decoding device decodes theencoding result of the color, and thereafter decodes the encoding resultof the reflectance. Note that the three-dimensional data decoding devicemay decode the encoding results of the attribute information added to abitstream in any order.

Additionally, the three-dimensional decoding device may obtain theinformation indicating the start location of the encoded data of eachattribute information in a bitstream by decoding a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount of thethree-dimensional decoding device can be reduced. Additionally, thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and may integrate the decodingresults into one three-dimensional point cloud. Accordingly, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at high speed.

FIG. 85 is a flowchart of the attribute information decoding processing(S6632). First, the three-dimensional decoding device decodes a codingcoefficient from a bitstream (S6641). Next, the three-dimensionaldecoding device applies the inverse quantization to the codingcoefficient (S6642). Next, the three-dimensional decoding device decodesthe attribute information by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization (S6643).

Embodiment 11

FIG. 86 is a diagram for illustrating a schematic configuration of anattribute information encoder according to this embodiment. FIG. 87 is adiagram for illustrating a schematic configuration of an attributeinformation decoder according to this embodiment.

To improve the coding efficiency, attribute information in the pointcloud compression (PCC) is transformed in various manners (such aslifting, RAHT, or other transformation processes). The transformationprocess has a strong “energy compression” property. As a result of thetransformation process, important signal information is included in alow frequency component. A high frequency component is quantized inorder to reduce the number of bits that occur.

In this embodiment, in order to further improve the coding efficiency,that is, in order to minimize the high frequency components that are tobe transformed by maximizing the relationship between the positions ofthree-dimensional points, the three-dimensional data encoding devicemodifies the order of the three-dimensional points in input point clouddata (referred to also simply as a point cloud, hereinafter) or, morespecifically, the order of the transformation process performed on thethree-dimensional points by using geometry information.

First, point cloud re-ordering unit 7401 of the three-dimensional dataencoding device performs a process (re-ordering process (re-ordering))of modifying the order of the three-dimensional points in input pointcloud data, in which a plurality of three-dimensional points arearranged in a predetermined order. For example, pieces of data thatindicate the three-dimensional points in a point cloud input to thethree-dimensional data encoding device are arranged in a predeterminedorder. Point cloud re-ordering unit 7401 re-orders the pieces of dataindicating the three-dimensional points in the input point cloud in apredetermined manner.

Transformer 7402 of the three-dimensional data encoding device thenperforms a transformation process (transform) on the point cloudre-ordered by the re-ordering process.

Quantizer 7403 of the three-dimensional data encoding device thenperforms a quantization process.

Entropy encoder 7404 of the three-dimensional data encoding device thenperforms an entropy encoding process. For example, entropy encoder 7404transmits a bitstream (encoded bitstream) including encoded point clouddata to the three-dimensional data decoding device.

Entropy decoder 7411 of the three-dimensional data decoding deviceperforms an entropy decoding process on the encoded point cloud dataincluded in the bitstream received from the three-dimensional dataencoding device, for example.

Inverse quantizer 7412 of the three-dimensional data decoding devicethen performs an inverse quantization process.

Inverse transformer 7414 of the three-dimensional data decoding devicethen performs an inverse transformation process (inverse transform).

Point cloud re-ordering unit 7413 of the three-dimensional data decodingdevice then performs a re-ordering process (ordering process) on thepoint cloud data having been subjected to the inverse transformationprocess. The re-ordering process here is a reverse process to there-ordering process in the three-dimensional data encoding device. Byre-ordering the decoded point cloud data in this way, thethree-dimensional data decoding device can generate point cloud data inwhich the three-dimensional points are arranged in the same order asthose in the point cloud data input to the three-dimensional dataencoding device. The three-dimensional data decoding device transmits(outputs) the point cloud data subjected to the re-ordering process toanother device, for example. In this way, the other device can obtainpoint cloud data in which the three-dimensional points are arranged inthe same order as those in the point cloud data input to thethree-dimensional data encoding device.

Note that the three-dimensional data decoding device may perform there-ordering process for the point cloud in the same manner as in thethree-dimensional data encoding device after performing the inversetransformation process.

For example, the three-dimensional data encoding device performs there-ordering process by generating re-ordering information that,indicates the order of points in a point cloud re-ordered using geometryinformation obtained by encoding and decoding of the point cloud. Forexample, the three-dimensional data decoding device performs there-ordering process by generating re-ordering information in the samemanner as in the three-dimensional data encoding device using thedecoded geometry information.

In this way, the three-dimensional data decoding device can generate andoutput a point cloud (point cloud data) in which pieces of data arearranged in the same order as the pieces of data in the point cloudinput to the three-dimensional data encoding device.

Note that when the three-dimensional data decoding device does not needto generate a point cloud arranged in the same order as the point cloudinput to the three-dimensional data encoding device, thethree-dimensional data decoding device can omit the re-ordering process.

In this way the three-dimensional data decoding device can reduce theprocessing amount.

The three-dimensional data encoding device may add the re-orderinginformation to the bitstream. The re-ordering information is informationthat indicates the data order of pieces of attribute information on aplurality of three-dimensional points in the point cloud data input tothe three-dimensional data encoding device (that is, the point clouddata yet to be re-ordered). The three-dimensional data decoding devicemay perform the re-ordering process based on the re-ordering informationdecoded from the bitstream.

In this way, the three-dimensional data decoding device can reduce theamount of processing for generating the re-ordering information.

The method of modifying the ordering of the point cloud is not limitedto the method described above.

FIG. 88 is a diagram for illustrating a schematic configuration of anattribute information encoder according to a variation of thisembodiment. FIG. 89 is a diagram for illustrating a schematicconfiguration of an attribute information decoder according to avariation this embodiment.

To improve the coding efficiency, attribute information in the PCC istransformed in various manners (such as lifting, RAHT, or othertransformation processes). The transformation process has a strong“energy compression” property. As a result of the transformationprocess, important signal information is included in a low frequencycomponent. A high frequency component is quantized in order to reducethe number of bits that occur.

In order to further improve the coding efficiency, the three-dimensionaldata encoding device modifies the order of points in an input pointcloud (point cloud data) using geometry information to maximize therelationship between the positions of three-dimensional points tominimize the high frequency components that are to be transformed.

First, point cloud attribute swapper 7421 of the three-dimensional dataencoding device performs a process (swapping process) of modifying theorder of the pieces of attribute information on the three-dimensionalpoints in input point cloud data (referred to also simply as a pointcloud, hereinafter), in which a plurality of three-dimensional pointsare arranged in a predetermined order. The swapping process (swapping)is an example of the re-ordering process. For example, pieces of datathat indicate the three-dimensional points in a point cloud input to thethree-dimensional data encoding device are arranged in a predeterminedorder (such as an order of Morton codes). Point cloud attribute swapper7421 re-orders only the pieces of attribute information in apredetermined manner, without modifying the order of the Morton codes,the pieces of geometry information or the like in the input point clouddata indicating the three-dimensional points in the point cloud, forexample.

For example, point cloud attribute swapper 7421 performs the swappingprocess of swapping pieces of attribute information without changing theMorton codes assigned to the three-dimensional points, before performingthe transformation process for the attribute information of the pointcloud.

For example, when Morton codes 0, 1, and 2 are assigned tothree-dimensional points A, B, and C, respectively if thethree-dimensional data encoding device determines that point A and pointC are close to each other based on the geometry information or the like,the three-dimensional data encoding device performs a swapping processof swapping pieces of attribute information on point B and point Cbefore performing a transformation process, such as RAHT, using theattribute information on point A and point B.

In this way the three-dimensional data encoding device can reduce thecoefficient of a high frequency component subjected to thetransformation process, and therefore can improve the coding efficiency.

Note that the three-dimensional data encoding device may add, to theheader or the like of the bitstream, swapping information that indicatesthe way in which the three-dimensional points have been swapped.

In this way, the three-dimensional data decoding device canappropriately reassign the pieces of attribute information to thethree-dimensional points (that is, perform the re-ordering process)using the swapping information decoded from the header of the bitstreamafter the inverse transformation process.

Note that the three-dimensional data encoding device may add theswapping information to the header using variable length encoding or thelike.

In this way, the three-dimensional data encoding device can reduce theheader amount.

Transformer 7422 of the three-dimensional data encoding device thenperforms a transformation process on the point cloud re-ordered by theswapping process.

Quantizer 7423 of the three-dimensional data encoding device thenperforms a quantization process.

Entropy encoder 7424 of the three-dimensional data encoding device thenperforms an entropy encoding process. For example, entropy encoder 7424transmits a bitstream including encoded point cloud data to thethree-dimensional data decoding device.

Entropy decoder 7431 of the three-dimensional data decoding deviceperforms an entropy decoding process on the encoded point cloud dataincluded in the bitstream received from the three-dimensional dataencoding device, for example.

Inverse quantizer 7432 of the three-dimensional data decoding devicethen performs an inverse quantization process.

Inverse transformer 7433 of the three-dimensional data decoding devicethen performs an inverse transformation process.

After the inverse transformation process is performed, point cloudattribute swapper 7434 of the three-dimensional data decoding devicethen performs a swapping process of swapping pieces of attributeinformation in the same manner as in the three-dimensional data encodingdevice, for example.

For example, when Morton codes 0, 1, and 2 are assigned to decodedthree-dimensional points A, B, and C, respectively, if thethree-dimensional data decoding device determines that point A and pointC are close to each other based on the decoded geometry information orthe like, the three-dimensional data decoding device swaps the pieces ofdecoded attribute information on point B and. point C with each other.

In this way, the three-dimensional data decoding device can decodethree-dimensional points with appropriate attribute information assignedthereto. The three-dimensional data decoding device can also generateand output a point cloud (point cloud data) in which pieces of data arearranged in the same order as the pieces of data in the point cloudinput to the three-dimensional data encoding device.

Note that when the three-dimensional data decoding device does not needto generate a point cloud arranged in the same order as the point cloudinput to the three-dimensional data encoding device, thethree-dimensional data decoding device can omit the swapping process.

In this way, the three-dimensional data decoding device can reduce theprocessing amount.

The three-dimensional data encoding device may add the swappinginformation to the bitstream. The three-dimensional data decoding devicemay perform the swapping process based on the swapping informationdecoded from the bitstream.

In this way, the three-dimensional data decoding device can reduce theamount of processing for generating the swapping information.

FIG. 90 is a diagram for illustrating the re-ordering process accordingto this embodiment. Specifically part (a) of FIG. 90 is a diagramshowing an example of a plurality of voxels (specifically, eight voxels)and Morton codes assigned to the plurality of voxels. Part (b) of FIG.90 is a diagram showing another example of a plurality of voxels(specifically, eight voxels) and Morton codes assigned to the pluralityof voxels. Part (c) of FIG. 90 is a diagram showing an example of anordering of a point cloud obtained by modifying the ordering of thepoint cloud shown in part (b) of FIG. 90.

In each of parts (a) and (b) of FIG. 90, the left part shows theplurality of voxels, and the right part shows the ordering of the pointcloud. Note that the ordering of the point cloud is indicated by Mortoncodes.

As shown in parts (a) and (b) of FIG. 90, for example, the point cloudis arranged in a Z order or Morton order before the transformationprocess is performed. Here, in part (b) of FIG. 90, for example, inorder to effectively perform the transformation process, thethree-dimensional data encoding device performs the re-ordering processto further re-order the point cloud arranged in a Morton order based onthe geometry information on the three-dimensional points as shown inpart (c) of FIG. 90, for example.

Part (a) of FIG. 90 shows an example of a point cloud arranged in aMorton order. Specifically the example shown in part (a) of FIG. 90 isan example in which each voxel includes one three-dimensional point. Inthis case, for example, the Morton order of the point cloud is 0, 1, 2,3, 4, 5, 6, 7, and the point cloud is arranged in this order.

On the other hand, part (b) of FIG. 90 shows a Morton order in the casewhere all the voxels are not occupied by a three-dimensional point.Specifically, voxel 1 and voxel 2 are not occupied by anythree-dimensional point. In this case, for example, the Morton order ofthe point cloud is 0, 3, 4, 5, 6, 7, and the point cloud is arranged inthis order.

The attribute information concerning the position of a three-dimensionalpoint is obtained from the surface of a three-dimensional object, forexample. Therefore, attribute information on three-dimensional points(closest three-dimensional points) whose surfaces are the closest toeach other are highly correlated to each other. That is, attributeinformation on three-dimensional points that are close to each other arelikely to have values close to each other. Therefore, thethree-dimensional data encoding device performs the re-ordering processof re-ordering the point cloud so that such three-dimensional points atclose positions are brought closer to each other, before performing thetransformation process. For example, the three-dimensional data encodingdevice re-orders the point cloud from the order of 0, 3, 4, 5, 6, 7shown in part (b) of FIG. 90 to the order of 0, 4, 3, 5, 6, 7 shown inpart (c) of FIG. 90. That is, the three-dimensional data encoding deviceswaps the positions of the pieces of data (geometry information,attribute information or the like) on the three-dimensional pointassigned with a Morton code of 3 and the three-dimensional pointassigned with a Morton code of 4 with each other.

Note that the “re-ordering process” in this example may refer to aprocess of generating re-ordering information that indicates the orderof a new point cloud generated by performing the re-ordering process ona point cloud arranged in a Morton order using distance information,geometry information or the like on three-dimensional points, forexample.

FIG. 91 is a diagram for illustrating a first example of thetransformation process for attribute information according to thisembodiment. FIG. 91 shows a hierarchical structure with which thethree-dimensional data encoding device performs the transformationprocess, for example.

The three-dimensional data encoding device performs the transformationprocess on the point cloud subjected to the re-ordering process. Thetransformation process is RAHT (region adaptive Haar transformation),for example.

The transformation process is expressed by the following formula(Equation M1), for example.

$\begin{matrix}{\begin{bmatrix}L_{l,m} \\H_{l,m}\end{bmatrix} = {\begin{bmatrix}\alpha & \beta \\{- \beta} & \alpha\end{bmatrix}\begin{bmatrix}C_{{l + 1},{2m}} \\C_{{l + 1},{{2\; m} + 1}}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu} M\; 1} \right)\end{matrix}$

α and β each represent an arbitrary number, and coefficients(transformation coefficients) represented by α and β can be updated. lrepresents a value that indicates a level of a layer. m represents avalue that indicates the order of three-dimensional points in eachlayer. Cl, m represents a value that indicates attribute information onthe m-th three-dimensional point at level 1.

A low pass sub-band (low frequency component) at level 1 is expressed bythe following formula (Equation M2).

L _(l,m) =αC _(l+1,2m) +βC _(l+a,2m+1)   (Equation M2)

A high pass sub-band (high frequency component) at level 1 is expressedby the following formula (Equation M3).

H _(l,m) =αC _(l+1,2m+1) −βC _(l+1,2m)   (Equation M3)

The high pass sub-band is quantized and entropy-encoded. On the otherhand, the low pass sub-band is moved to the next level as shown by thefollowing formula (Equation M4).

C_(l,m)=L_(l,m)   (Equation M4)

For example, the low pass sub-band and the high pass sub-band for0-th(m=0) at level l=2 is expressed by the following formulas (EquationM5) and (Equation M6), respectively.

L _(2,0) =αC _(3,0) +βC _(3,1)   (Equation M5)

H _(2,0) =αC _(3,1) −βC _(3,0)   (Equation M6)

For example, when the positions of three-dimensional points are close toeach other, such as those having attribute information C3,0 andattribute information C3,1, those pieces of attribute information arelikely to be similar (or likely to have similar attribute values).Therefore, H, which is the value of the high pass sub-band calculatedfrom the difference between the attribute information C3, 0 and theattribute information C3, 1, is likely to be small.

FIG. 92 is a diagram for illustrating a second example of thetransformation process for attribute information according to thisembodiment. Specifically, part (a) of FIG. 92 is a diagram showing anordering of point cloud data yet to be subjected to the re-orderingprocess, and part (b) of FIG. 92 is a diagram showing an ordering ofpoint cloud data obtained by performing the re-ordering process on thepoint cloud data shown in part (a) of FIG. 92.

In order to further improve the coding efficiency of attributeinformation, for example, the three-dimensional data encoding deviceperforms the re-ordering process on attribute information based ondistance or geometry information on three-dimensional points.

For example, as shown in part (a) of FIG. 92, it is assumed that (n+1)pieces of point data are arranged in ascending order of Morton codes of0 to n.

For example, the three-dimensional data encoding device performs there-ordering process of re-ordering the order of the Morton codes (thatis, the pieces of data on three-dimensional points) based on distance orgeometry information on three-dimensional points.

In this way, for example, the three-dimensional data encoding devicegenerates new point cloud data in which the position of the data on thethree-dimensional point assigned with a Morton code of 1 and theposition of the data on the three-dimensional point assigned with aMorton code of 2 are swapped with each other, and the position of thedata on the three-dimensional point assigned with a Morton code of 7 andthe position of the data on the three-dimensional point assigned with aMorton code of 9 are swapped with each other.

Alternatively, the three-dimensional data encoding device may performthe re-ordering process of modifying the order of the pieces ofattribute information, and retain re-ordering information that allowsencoding and decoding as additional information (Meta data).

FIG. 93 is a diagram for illustrating a third example of thetransformation process for attribute information according to thisembodiment. Part (a) of FIG. 93 is a diagram showing an ordering ofpoint cloud data yet to be subjected to the re-ordering process, andpart (b) of FIG. 93 is a diagram showing an ordering of point cloud dataobtained by performing the re-ordering process on the point cloud datashown in part (a) of FIG. 93. Note that FIG. 93 is a diagram showing anexample in which a point cloud is re-ordered based on the distances(distance information) between three-dimensional points. For example, asshown in part (a) of FIG. 93, it is assumed that (n+1) pieces of pointdata are arranged in ascending order of Morton codes of 0 to n.

First, the three-dimensional data encoding device designates thethree-dimensional point located at position 0 (that is, thethree-dimensional point assigned with a Morton code of 0) as a referencepoint, and searches (by calculation) k three-dimensional pointsneighboring to the reference point (k=5 in this example) for thethree-dimensional point closest to the reference point (closestthree-dimensional point). In this example, it is assumed that thethree-dimensional point located at position 2 is the three-dimensionalpoint closest to position 0. In this case, for example, thethree-dimensional data encoding device moves the data on thethree-dimensional point located at position 2 to next to position 0,which is the position of the reference point.

Note that the three-dimensional data encoding device may add k, whichindicates the number of the three-dimensional points in the searchrange, to the header of the bitstream.

In this way, the three-dimensional data decoding device can perform there-ordering process for the three-dimensional points using the samesearch range as the three-dimensional data encoding device by decodingsearch range k included in the header of the bitstream.

The three-dimensional data encoding device then designates thethree-dimensional point located at position 1 as a reference point, andsearches k three-dimensional points neighboring to the reference pointfor the closest three-dimensional point. In this example, it is assumedthat the three-dimensional point located at position 3 is thethree-dimensional point closest to position 1. In this case, forexample, the data on the three-dimensional point located at position 3is located next to the data on the three-dimensional point located atposition 1, which is the reference point, so that the three-dimensionaldata encoding device then designates the three-dimensional point locatedat position 3 as a new reference point, and searches for thethree-dimensional point closest to the new reference point.

As described above, the three-dimensional data encoding device performsthe setting of a reference point and the searching for thethree-dimensional point closest to the set reference point until thethree-dimensional point located at position n is reached, for example.

FIG. 94 is a diagram for illustrating a fourth example of thetransformation process for attribute information according to thisembodiment. Part (a) of FIG. 94 is a diagram showing an ordering ofpoint cloud data yet to be subjected to the re-ordering process, andpart (b) of FIG. 94 is a diagram showing an ordering of point cloud dataobtained by performing the re-ordering process on the point cloud datashown in part (a) of FIG. 94.

Note that FIG. 94 is a diagram showing an example in which a point cloudis re-ordered based on the distances (distance information) betweenthree-dimensional points. For example, as shown in part (a) of FIG. 94,it is assumed that (n+1) pieces of point data are arranged in ascendingorder of Morton codes of 0 to n.

First, the three-dimensional data encoding device designates thethree-dimensional point located at position 0 as a reference point, andsearches k three-dimensional points close to the reference point for theclosest three-dimensional point. In this example, it is assumed that thethree-dimensional point located at position 24 is the three-dimensionalpoint closest to position 0.

For example, the three-dimensional data encoding device then designatesthe three-dimensional point located at position 24 as a reference point,and searches k three-dimensional points close to the reference point forthe closest three-dimensional point. The three-dimensional data encodingdevice performs the re-ordering process described above for all thethree-dimensional points one by one.

Note that the three-dimensional data encoding device may perform there-ordering process for the point cloud based on Morton codes.Alternatively, the three-dimensional data encoding device may performthe re-ordering process only on the attribute information on thethree-dimensional points, and maintain the positions of the Mortoncodes. Alternatively, the three-dimensional data encoding device maygenerate swapping table information (referred to also as a re-orderingtable), which is swapping information on a point cloud that indicatesthe positions of the three-dimensional points in the point cloud yet tobe subjected to the re-ordering process.

As described above, for example, the three-dimensional data encodingdevice performs the re-ordering process on a point cloud based onthree-dimensional distances. For example, as shown in part (a) of FIG.94, it is assumed that (n+1) pieces of point data are arranged inascending order of Morton codes of 0 to n. For example, thethree-dimensional data encoding device designates the three-dimensionalpoint located at position 0 as a reference point, and searches kthree-dimensional points close to the reference point for the closestthree-dimensional point.

The three-dimensional data encoding device then expresses X0=(x0, y0,z0) for the three-dimensional point at position 0 as Xi=(xi, yi, zi) forthe three-dimensional point at position i.

Among the k three-dimensional points close to the three-dimensionalpoint located at position 0, the three-dimensional point closest to thethree-dimensional point located at position 0 can be determined bysearching for the minimum value of the Euclidean distances between Ithree-dimensional points as shown by the following formulas (EquationM7) and (Equation M8).

$\begin{matrix}{{\min\limits_{i}{{X_{0} - X_{i}}}_{2}},{{{where}\mspace{14mu} i} \in \left\{ {1,\ldots\mspace{14mu},k} \right\}}} & \left( {{Equation}\mspace{14mu} M\; 7} \right) \\{{{X_{0} - X_{i}}}_{2} = \sqrt{\left( {x_{0} - x_{i}} \right)^{2} + \left( {y_{0} - y_{i}} \right)^{2} + \left( {z_{0} - z_{i}} \right)^{2}}} & \left( {{Equation}\mspace{14mu} M\; 8} \right)\end{matrix}$

Once the closest three-dimensional point is determined, the closestthree-dimensional point is moved from position i to position 1. Notethat another method may be used for determining the three-dimensionaldistances.

FIG. 95 is a diagram for illustrating a fifth example of thetransformation process for attribute information according to thisembodiment. FIG. 96 is a diagram for illustrating examples of aconnection between voxels and normal vectors in the examples accordingto this embodiment.

Note that FIGS. 95 and 96 are diagrams showing an example in which apoint cloud is re-ordered using geometry information onthree-dimensional points.

Neighboring three-dimensional points on the surface of the same objectare likely to have similar attribute information, and therefore, theordering of the point cloud data is modified so that the pieces of dataon the neighboring three-dimensional points are close to each otherbefore the transformation process in order to improve the codingefficiency. For example, a reference point (voxel) Cr that hasneighboring three-dimensional points Ca, Cb, Cc, Cd, Ce, Cf and Cg willbe considered. Geometry information, such as a normal vector

{right arrow over (N_(r))}  (Equation M9)

is used to determine a. surface of a three-dimensional point.

For example, in the re-ordering process, points Ca, Cb, and Cd have anormal vector oriented in the same direction as the normal vector ofpoint Cr and is of a connection type in which the objects of the pointsare connected to the object of point Cr, so that the point cloud data isre-ordered so that the pieces of data on these points are close to pointCr.

The normal vector (Equation M9) of point Cr is calculated according tothe following formula (Equation M10).

{right arrow over (N _(r))}=(C _(b) −C _(r))×(C _(a) −C _(r))  (Equation M10)

Note that “×” in the formula (Equation M10) represents vector product.

Voxels may be grouped based on the normal vector and the objectconnection type. In that case, for example, the three-dimensional pointsin the point cloud data are re-ordered based on the groups resultingfrom the grouping.

As shown in FIG. 96, connection types include a stair-like type(Stair-like), a convex type (Convex), and a concave type (Concave), forexample.

For example, the connection types described above are used for groupingfor the re-ordering process. For example, three-dimensional pointsrelated in the convex connection type are grouped into the same group.On the other hand, for example, three-dimensional points related in theconcave connection type or stair-like connection type are not groupedinto the same group. Three-dimensional points related in the concaveconnection type or stair-like connection type are likely to belong todifferent objects or have different attribute information. For example,the concave connection type is likely to be more strongly affected by ashadow than the convex connection type. Therefore, in the grouping of apoint cloud, three-dimensional points related in the concave connectiontype are not grouped into the same group but separated from each other.

Note that although an example has been described in which thethree-dimensional data encoding device calculates perpendicular vectors(normal vectors) and uses the calculated normal vectors for there-ordering process, the present disclosure is not necessarily limitedthereto. For example, when encoding and decoding normal vectors asattribute information, the three-dimensional data encoding device mayperform the re-ordering process for a point cloud by performing thegrouping described above using the values of the attribute information.

Note that the three-dimensional data encoding device may generate there-ordering information on the three-dimensional points yet to besubjected to the transformation process or the swapping information onthe attribute information on the three-dimensional points usinginformation used for encoding of the geometry information. For example,as described above, the three-dimensional data encoding device generatesthe re-ordering information or swapping information using neighboringnode information calculated when encoding the occupancy code for eachnode for the geometry information.

In this example, there is a possibility that the three-dimensional dataencoding device generates information that indicates that a nodeassigned with a Morton code of 1 is not occupied and a node assignedwith a Morton code of 4 is occupied when encoding an occupancy code fora node assigned with a Morton code of 0, for example. Therefore, basedon the information, the three-dimensional data encoding device maychoose the node assigned with the Morton code of 4 as a pair to the nodeassigned with the Morton code of 0 and apply the transformation to thenode. Alternatively, the attribute information on the node assigned withthe Morton code of 1 and the attribute information on the node assignedwith the Morton code of 4 may be swapped with each other, and thetransformation process may be performed on the node assigned with theMorton code of 0 and the node assigned with the Morton code of 1.

In this way, the three-dimensional data encoding device may perform thetransformation process by generating re-ordering information forthree-dimensional points or swapping information for attributeinformation using information generated when encoding geometryinformation.

With such a configuration, the three-dimensional data encoding devicecan improve the coding efficiency while reducing the amount ofprocessing for generation of the re-ordering information or swappinginformation.

The three-dimensional data decoding device may generate re-orderinginformation for three-dimensional points subjected to the inversetransformation process or swapping information for attribute informationon three-dimensional points subjected to the inverse transformationprocess using information used when decoding geometry information.

For example, as described above, the three-dimensional data decodingdevice generates re-ordering information or swapping information usingneighboring node information calculated when decoding an occupancy codefor each node for geometry information.

In this example, there is a possibility that the three-dimensional datadecoding device generates information that indicates that a nodeassigned with a Morton code of 1 is not occupied and a node assignedwith a Morton code of 4 is occupied when decoding an occupancy code fora node assigned with a Morton code of 0. Therefore, based on theinformation, the three-dimensional data decoding device may swap thethree-dimensional point assigned with the Morton code of 1 subjected tothe inverse transformation process and the three-dimensional pointassigned with the Morton code of 4 subjected to the inversetransformation process with each other. Alternatively, thethree-dimensional data decoding device may swap the attributeinformation on the node assigned with the Morton code of 1 and theattribute information on the node assigned with the Morton code of 4after the inverse transformation.

In this way, the three-dimensional data decoding device can properlydecode the bitstream encoded with improved coding efficiency, whilereducing the processing amount for the generation of re-orderinginformation or swapping information by swapping three-dimensional pointsor swapping pieces of attribute information after generating andinverse-transforming re-ordering information for the three-dimensionalpoints or swapping information for the attribute information based oninformation generated in the decoding of geometry information.

FIG. 97 is a diagram for illustrating a sixth example of thetransformation process for attribute information according to thisembodiment.

The three-dimensional data encoding device may perform the re-orderingprocess for each layer. Alternatively, the three-dimensional dataencoding device may calculate a three-dimensional position used for there-ordering process for a layer based on a three-dimensional position ina layer lower than that layer.

For example, the three-dimensional position of each three-dimensionalpoint in layer 2 (level l=2) is calculated according to the followingformulas (Equation M11), (Equation M12), and (Equation M13).

C _(2,0)=(C _(3,0) +C _(3,1))/2   (Equation M11)

C _(2,1)=(C _(3,2) +C _(3,3))/2   (Equation M12)

C _(2,2)=(C _(3,4) +C _(3,5))/2   (Equation M13)

For example, the three-dimensional position of each three-dimensionalpoint in layer 1 (level l=1) is calculated according to the followingformulas (Equation M14) and (Equation M15).

C _(1,0)=(C _(2,0) +C _(2,1))/2   (Equation M14)

C_(1,1)=C_(2,2)   (Equation M15)

FIG. 98 is a diagram for illustrating a seventh example of thetransformation process for attribute information according to thisembodiment. Note that in the point cloud data shown in FIG. 98, there isno three-dimensional point at a position assigned with a Morton code of5.

Each three-dimensional point that is not paired is not merged with anyother three-dimensional point during the transformation process. There-ordering process does not need to be performed for such athree-dimensional point. For example, the three-dimensional pointlocated at position 0 has a valid pair to the three-dimensional point,and the re-ordering process is performed on the three-dimensional pointlocated at position 4, which is the closest three-dimensional point. Onthe other hand, the three-dimensional point located at position 3 has novalid pair to the three-dimensional point, since there is nothree-dimensional point at position 5. In order to prevent thethree-dimensional point located at such position 3 from being used forthe transformation process, no closest three-dimensional point need tobe searched for.

To calculate which three-dimensional point has a valid pair for thetransformation process, for example, the following method is used.

Provided that the i-th Morton code at level l is denoted as Ml, i,

-   If(Ml, i>>1)==1){-   Find the nearest point to pair with the point at Ml, i.-   Skip 1 position and move to next point.-   } else

For example, if the conditional expression holds for a three-dimensionalpoint, the three-dimensional point has a valid pair for thetransformation process, the three-dimensional data encoding devicesearches for the closest three-dimensional point. On the other hand, ifthe conditional expression does not hold for a three-dimensional point,for example, the three-dimensional point has no valid pair for thetransformation process, the three-dimensional data encoding device doesnot need to search for the closest three-dimensional point. Therefore,with such a configuration, the three-dimensional data encoding devicedoes not need to search for the closest three-dimensional point for apoint such as a reference point to perform the re-ordering process.Therefore, the three-dimensional data encoding device can immediatelyproceed to process the next three-dimensional point.

FIG. 99 is a block diagram of three-dimensional data encoding device7440 according to this embodiment.

Three-dimensional data encoding device 7440 includes geometryinformation encoder 7441, attribute information encoder 7442, additionalinformation encoder 7443, and multiplexer (MUX) 7444.

Geometry information encoder 7441 encodes geometry information in pointcloud data input to three-dimensional data encoding device 7440.Geometry information encoder 7441 outputs the geometry informationencoded (encoded geometry information) to multiplexer 7444.

Attribute information encoder 7442 encodes attribute information in thepoint cloud data input to three-dimensional data encoding device 7440.Attribute information encoder 7442 outputs the attribute informationencoded (encoded attribute information) to multiplexer 7444.

Additional information encoder 7443 encodes additional information inthe point cloud data input to three-dimensional data encoding device7440. Additional information encoder 7443 outputs the additionalinformation encoded (encoded additional formation) to multiplexer 7444.

Multiplexer 7444 generates and outputs a bitstream including the encodedgeometry information, the encoded attribute information, and the encodedadditional information. For example, multiplexer 7444 outputs thebitstream to a three-dimensional data decoding device.

FIG. 100 is a block diagram of attribute information encoder 7442according to this embodiment.

Attribute information encoder 7442 includes point cloud re-ordering unit74421, transformer 74422, quantizer 74423, and entropy encoder 74424.

Point cloud re-ordering unit 74421 performs a re-ordering process ofre-ordering the data order of the point cloud data input tothree-dimensional data encoding device 7440. As described above, pointcloud re-ordering unit 74421 re-orders the order of pieces of attributeinformation based on geometry information, for example.

Transformer 74422 performs a transformation process for the attributeinformation in the re-ordered point cloud data.

Quantizer 74423 performs a quantization process on the point cloud datasubjected to the transformation process.

Entropy encoder 74424 performs an entropy-encoding process on thequantized point cloud data.

FIG. 101 is a block diagram of point cloud re-ordering unit 74421according to this embodiment.

Point cloud re-ordering unit 74421 includes Morton ordering unit 744211and re-ordering unit 744212.

Morton ordering unit 744211 re-orders the pieces of attributeinformation in the input point cloud data in a Morton order.

Re-ordering unit 744212 re-orders the point cloud data re-ordered in theMorton order based on geometry information or three-dimensionaldistances as described above.

FIG. 102 is a block diagram of three-dimensional data decoding device7450 according to this embodiment.

Three-dimensional data decoding device 7450 includes demultiplexer(DeMUX) 7451, geometry information decoder 7452, attribute informationdecoder 7453, and additional information decoder 7454.

Demultiplexer 7451 divides the bitstream into the encoded geometryinformation, the encoded attribute information, and the encodedadditional information and outputs the encoded geometry information, theencoded attribute information, and the encoded additional information.Specifically, demultiplexer 7451 outputs the encoded geometryinformation included in the bitstream to geometry information decoder7452, outputs the encoded attribute information included in thebitstream to attribute information decoder 7453, and outputs the encodedadditional information included in the bitstream to additionalinformation decoder 7454.

Geometry information decoder 7452 decodes the encoded geometryinformation to generate geometry information, and outputs the generatedgeometry information.

Attribute information decoder 7453 decodes the encoded attributeinformation to generate attribute information, and outputs the generatedattribute information.

Additional information decoder 7454 decodes the encoded additionalinformation to generate additional information, and outputs thegenerated additional information.

FIG. 103 is a block diagram of attribute information decoder 7453according to this embodiment.

Attribute information decoder 7453 includes entropy decoder 74531,inverse quantizer 74532, point cloud re-ordering unit 74533, and inversetransformer 74534.

Entropy decoder 74531 performs a variable-length decoding of thebitstream. For example, entropy decoder 74531 arithmetically decodes theencoded attribute information to generate a binary signal, and generatesa quantization coefficient from the generated binary signal.

Inverse quantizer 74532 generates an inverse quantization coefficient byinverse-quantizing the quantization coefficient received from entropydecoder 74531 using the quantization parameter added to the bitstream orthe like.

Inverse transformer 74534 inverse-transforms the inverse quantizationcoefficient received from inverse quantizer 74532. For example, inversetransformer 74534 performs a reverse process to the process bytransformer 74422.

In this way, the same point cloud data as the point cloud datare-ordered by three-dimensional data encoding device 7440 is generated.

Point cloud re-ordering unit 74533 re-orders the pieces of attributeinformation in the point cloud data by performing a re-ordering processon the point cloud data generated by inverse transformer 74534. Forexample, point cloud re-ordering unit 74533 performs a reverse processto the process by point cloud re-ordering unit 74421. In this way, pointcloud data in which the pieces of data are arranged in the same order asthose in the point cloud data input to the three-dimensional dataencoding device is generated.

FIG. 104 is a block diagram of three-dimensional data encoding device7460 according to a variation of this embodiment.

Three-dimensional data encoding device 7460 includes geometryinformation encoder 7461, attribute information encoder 7462, additionalinformation encoder 7463, and multiplexer 7464.

Geometry information encoder 7461 encodes geometry information in pointcloud data input to three-dimensional data encoding device 7460.Geometry information encoder 7461 outputs the geometry informationencoded (encoded geometry information) to multiplexer 7464.

Attribute information encoder 7462 encodes attribute information in thepoint cloud data input to three-dimensional data encoding device 7460.Attribute information encoder 7462 outputs the attribute informationencoded (encoded attribute information) to multiplexer 7464.

Attribute information encoder 7462 also modifies the order of pieces ofdata in the point cloud data before encoding the attribute information.Attribute information encoder 7462 generates a re-ordering table (anexample of the swapping information described above) that indicates theorder of the pieces of data yet to be modified (yet to be re-ordered),encodes the generated re-ordering table, and outputs the encodedre-ordering table to multiplexer 7464.

Additional information encoder 7463 encodes additional information inthe point cloud data input to three-dimensional data encoding device7460. Additional information encoder 7463 outputs the additionalinformation encoded (encoded additional information) to multiplexer7464.

Multiplexer 7464 generates and outputs a bitstream including the encodedgeometry information, the encoded attribute information, the encodedre-ordering table, and the encoded additional information. For example,multiplexer 7464 outputs the bitstream to a three-dimensional datadecoding device.

FIG. 105 is a block diagram of attribute information encoder 7462according to a variation of this embodiment.

Attribute information encoder 7462 includes re-ordering table generator74621, transformer 74622, quantizer 74623, and entropy encoder 74624.

Re-ordering table generator 74621 performs a re-ordering process ofre-ordering the data order of the point cloud data input tothree-dimensional data encoding device 7460. As described above,re-ordering table generator 74621 re-orders the order of pieces ofattribute information based on geometry information, for example.Re-ordering table generator 74621 also generates the encoded re-orderingtable described above. Re-ordering table generator 74621 outputs thegenerated encoded re-ordering table to entropy encoder 74624, forexample.

Transformer 74622 performs a transformation process for the attributeinformation in the re-ordered point cloud data.

Quantizer 74623 performs a quantization process on the point cloud datasubjected to the transformation process.

Entropy encoder 74624 performs an entropy-encoding process on thequantized point cloud data. For example, entropy encoder 74624 outputsthe point cloud data subjected to the entropy encoding process and theencoded re-ordering table to multiplexer 7464.

As described above, the three-dimensional data encoding device maygenerate a re-ordering table based on a transformation process and usethe re-ordering table. The three-dimensional data encoding device mayencode the generated re-ordering table and transmit the encodedre-ordering table to the three-dimensional data decoding device.

In this way, the three-dimensional data decoding device can more quicklyperform the decoding process.

FIG. 106 is a block diagram of three-dimensional data decoding device7470 according to a variation of this embodiment.

Three-dimensional data decoding device 7470 includes demultiplexer 7471,geometry information decoder 7472, attribute information decoder 7473,and additional information decoder 7474.

Demultiplexer 7471 divides the bitstream into the encoded geometryinformation, the encoded attribute information, the encoded re-orderingtable, and the encoded additional information and outputs the encodedgeometry information, the encoded attribute information, the encodedre-ordering table, and the encoded additional information. Specifically,demultiplexer 7471 outputs the encoded geometry information included inthe bitstream to geometry information decoder 7472, outputs the encodedattribute information and the encoded re-ordering table included in thebitstream to attribute information decoder 7473, and outputs the encodedadditional information included in the bitstream to additionalinformation decoder 7474.

Geometry information decoder 7472 decodes the encoded geometryinformation to generate geometry information, and outputs the generatedgeometry information.

Attribute information decoder 7473 decodes the encoded re-ordering tableto generate a re-ordering table. Attribute information decoder 7473 alsodecodes the encoded attribute information to generate attributeinformation, and outputs the generated attribute information.

Additional information decoder 7474 decodes the encoded additionalinformation to generate additional information, and outputs thegenerated additional information.

FIG. 107 is a block diagram of attribute information decoder 7473according to a variation of this embodiment.

Attribute information decoder 7473 includes entropy decoder 74731,inverse quantizer 74732, and inverse transformer 74733.

Entropy decoder 74731 performs a variable-length decoding of thebitstream. For example, entropy decoder 74731 arithmetically decodes theencoded attribute information to generate a binary signal, and generatesa quantization coefficient from the generated binary signal. Entropydecoder 74731 also decodes the encoded re-ordering table to generate are-ordering table, and outputs the generated re-ordering table toinverse transformer 74733.

Inverse quantizer 74732 generates an inverse quantization coefficient byinverse-quantizing the quantization coefficient received from entropydecoder 74731 using the quantization parameter added to the bitstream orthe like.

Inverse transformer 74733 inverse-transforms the inverse quantizationcoefficient received from inverse quantizer 74732. For example, inversetransformer 74733 performs a reverse process to the process bytransformer 74622.

In this way, the same point cloud data as the point cloud datare-ordered by three-dimensional data encoding device 7440 is generated.

Inverse transformer 74733 re-orders the pieces of attribute informationin the point cloud data by performing a re-ordering process on the pointcloud data generated by the inverse transformation process based on there-ordering table.

In this way, point cloud data in which the pieces of data are arrangedin the same order as those in the point cloud data input to thethree-dimensional data encoding device is generated.

FIG. 108 is a flowchart of a three-dimensional data encoding processaccording to this embodiment.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S7401). For example, the three-dimensional dataencoding device performs the encoding using an octree representation.

The three-dimensional data encoding device then performs atransformation process on attribute information (S7402). For example,after the encoding of geometry information, if the position of athree-dimensional point is changed because of quantization or the like,the three-dimensional data encoding device reassigns the attributeinformation on the original three-dimensional point to thethree-dimensional point changed in position.

Note that the three-dimensional data encoding device may perform thereassignment by interpolation of values of the attribute informationaccording to the amount of change in position. For example, thethree-dimensional data encoding device may detect N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, take aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determine theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation subjected to the transformation process (S7403).

Note that, when the three-dimensional data encoding device encodes aplurality of pieces of attribute information, the three-dimensional dataencoding device may sequentially encode the plurality of pieces ofattribute information. For example, when the three-dimensional dataencoding device encodes color and reflectance as attribute information,the three-dimensional data encoding device may generate a bitstreamincluding the result of encoding of color followed by the result ofencoding of reflectance.

Note that the order of the results of encoding of the attributeinformation added to the bitstream is not limited to the order describedabove, and can be any order.

The three-dimensional data encoding device may add a starting point ofthe encoded data of each attribute information in the bitstream to theheader or the like.

In this way, the three-dimensional data decoding device can selectivelydecode attribute information that needs to be decoded, and therefore canomit the decoding process for attribute information that does not needto be decoded. Therefore, the processing amount of the three-dimensionaldata decoding device can be reduced.

The three-dimensional data encoding device may encode a plurality ofpieces of attribute information in parallel, and integrate the resultsof the encoding into one bitstream.

In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 109 is a flowchart of the attribute information encoding process(S7403) according to this embodiment.

First, the three-dimensional data encoding device performs a re-orderingprocess on the attribute information in the input point cloud data(S7411).

The three-dimensional data encoding device then generates a codingcoefficient from the attribute information by Haar transformation asdescribed above, for example, and applies quantization to the generatedcoding coefficient (S7412). That is, the three-dimensional data encodingdevice generates a coding coefficient for the point cloud datare-ordered by the re-ordering process, and performs a quantizationprocess on the generated coding coefficient.

The three-dimensional data encoding device then encodes the quantizedcoding coefficient to generate encoded attribute information (S7413).

The three-dimensional data encoding device then applies inversequantization to the quantized coding coefficient (S7414).

The three-dimensional data encoding device then applies inverse Haartransformation to the inverse-quantized coding coefficient to decodeattribute information (S7415). For example, the decoded attributeinformation is referred to in the subsequent encoding.

FIG. 110 is a flowchart of the attribute information re-ordering process(S7411) according to this embodiment.

First, the three-dimensional data encoding device re-orders thethree-dimensional points in the input point cloud data in a Mortonorder, and assigns layer 0 to the three-dimensional points (S7421).

The three-dimensional data encoding device then sets i=0 (S7422).

The three-dimensional data encoding device then determines athree-dimensional point (neighboring three-dimensional point)neighboring to each three-dimensional point in layer i, and performs there-ordering process on each three-dimensional point so that thetransformation process can be applied to the three-dimensional point andthe neighboring three-dimensional point. Alternatively thethree-dimensional data encoding device performs the swapping process ofswapping the attribute values (values indicated by the attributeinformation) of the three-dimensional points (S7423).

For example, the three-dimensional data encoding device may re-orderthree-dimensional points or swap only attribute values indicated by theattribute information on three-dimensional points in the mannerdescribed above.

Note that threshold α may be provided in advance. In that case, forexample, the three-dimensional data encoding device may apply there-ordering process or swapping process when i<α, and does not need toapply the re-ordering process or swapping process when i is equal to orgreater than α. For example, by setting α=1 in advance, thethree-dimensional data encoding device may be configured to perform there-ordering process or swapping process for layer 0.

In this way the three-dimensional data encoding device can reduce theprocessing amount.

Note that the three-dimensional data encoding device may add the valueof α to the header or the like of the bitstream.

In this way, the three-dimensional data decoding device can determine upto which layer the re-ordering process or swapping process is to beperformed based on α added to the header or the like, and therefore canproperly decode the bitstream.

The three-dimensional data encoding device then calculates a highfrequency component and a low frequency component by applying thetransformation process to the attribute values of the three-dimensionalpoints assigned to layer i, designates the calculated high frequencycomponent as a coding coefficient, and sets the calculated low frequencycomponent to be a value for layer i+1 (S7424).

The three-dimensional data encoding device then sets i=i+1 (S7425).

The three-dimensional data encoding device then determines whether thenumber of three-dimensional points in layer i is 1 or not (S7426).

When the three-dimensional data encoding device determines that thenumber of three-dimensional points in layer i is not 1 (if No in S7426),the three-dimensional data encoding device returns the process to stepS7423.

On the other hand, when the three-dimensional data encoding devicedetermines that the number of three-dimensional points in layer i is 1(if Yes in S7426), the three-dimensional data encoding device sets thevalue of the three-dimensional point in layer i to be coding coefficient(S7427).

Note that, although an example has been shown above in which thethree-dimensional data encoding device repeats the loop (S7423 to S7426)until the number of the three-dimensional points in layer i becomes 1,the present disclosure is not necessarily limited thereto. For example,threshold β may be provided in advance. In that case, thethree-dimensional data encoding device may repeat the loop until thenumber of the three-dimensional points in layer i equals to β.

In this way, the three-dimensional data encoding device can reduce theprocessing amount.

Note that the three-dimensional data encoding device may add the valueof β to the header or the like of the bitstream.

In this way, the three-dimensional data decoding device can determine upto which layer the transformation process is to be performed based on βadded to the header or the like, and therefore can properly decode thebitstream.

FIG. 111 is a flowchart of a three-dimensional data decoding deviceaccording to this embodiment.

First, the three-dimensional data decoding device decodes geometryinformation (geometry) from the bitstream (S7431). For example, thethree-dimensional data decoding device performs the decoding using anoctree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7432). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.

Note that the three-dimensional data decoding device can decode theresults of encoding of attribute information in the bitstream in anyorder.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike.

In this way the three-dimensional data decoding device can selectivelydecode attribute information that needs to be decoded, and therefore canomit the decoding process for attribute information that does not needto be decoded. Therefore, the processing amount of the three-dimensionaldata decoding device can be reduced.

The three-dimensional data decoding device may decode a plurality ofpieces of attribute information in parallel, and integrate the resultsof the decoding into one three-dimensional point cloud.

In this way the three-dimensional data decoding device can decode aplurality of pieces of attribute information at a high speed.

FIG. 112 is a flowchart of the attribute information decoding process(S7432) according to this embodiment.

First, the three-dimensional data decoding device decodes the codingcoefficient from the bitstream (S7441).

The three-dimensional data decoding device then applies inversequantization to the coding coefficient (S7442).

The three-dimensional data decoding device then applies inverse Haartransformation to the inverse-quantized coding coefficient to decode theattribute information, and performs the re-ordering process on thedecoded attribute information (S7443).

FIG. 113 is a flowchart of the attribute information re-ordering process(S7443) according to this embodiment.

First, the three-dimensional data decoding device sets i=N (S7451).Here, N represents the number of layers, and is calculated based on thegeometry information on the three-dimensional points included in thepoint cloud data, for example.

The three-dimensional data decoding device then applies an inversetransformation process to the coding coefficient for layer i toreproduce the values of three-dimensional points in layer i (S7452).

The three-dimensional data decoding device then determines a neighboringthree-dimensional point of each three-dimensional point in layer i,generates the re-ordering information or swapping information, andperforms the re-ordering process of recovering the original positions ofthe attribute values that have been re-ordered or swapped by thethree-dimensional data encoding device (S7453).

For example, the three-dimensional data decoding device generates there-ordering information or the swapping information on the attributevalues indicated by the attribute information in the manner describedabove.

Note that threshold α may be provided in advance. In that case, thethree-dimensional data decoding device may generate the re-orderinginformation or swapping information when i<α, and does not need togenerate re-ordering information or swapping information when i is equalto or greater than α.

For example, by setting α=1 in advance, the three-dimensional datadecoding device may generate the re-ordering information or swappinginformation for layer 0.

In this way the three-dimensional data decoding device can reduce theprocessing amount.

Note that the three-dimensional data decoding device may decode andobtain the value of α added to the header or the like of the bitstream.

The three-dimensional data decoding device then sets i=i−1 (S7454).

The three-dimensional data decoding device then determines whether layeri is the lowermost layer or not (S7455).

When the three-dimensional data decoding device determines that layer iis not the lowermost layer (if No in S7455), the three-dimensional datadecoding device returns the process to step S7452.

On the other hand, when the three-dimensional data decoding devicedetermines that layer i is the lowermost layer (if Yes in S7455), thethree-dimensional data decoding device outputs the value of thethree-dimensional point in layer i as a decoded value (that is, anattribute value) (S7456).

Next, a three-dimensional data encoding device and a three-dimensionaldata decoding device according to a variation of this embodiment will bedescribed.

Although an example has been described above in which thethree-dimensional data encoding device uses distance information orgeometry information on three-dimensional points to re-order thethree-dimensional points or swaps pieces of attribute information on thethree-dimensional points before the transformation process, the presentdisclosure is not necessarily limited thereto.

For example, the point cloud data input to the three-dimensional dataencoding device may include information that indicates whether toperform the re-ordering process or not or information that indicateswhether to perform the swapping process or not added to the header orthe like thereof. In that case, the three-dimensional data encodingdevice may determine and choose whether to perform the re-orderingprocess or not or whether to perform the swapping process or not basedon the information.

Specifically, when giving priority to improving the coding efficiency,for example, the three-dimensional data encoding device performs there-ordering process or swapping process before the transformationprocess, adds a flag that indicates that the re-ordering process orswapping process has been performed to the header or the like, and turnon the flag.

On the other hand, when giving priority to reducing the processingamount, the three-dimensional data encoding device performs thetransformation process without performing the re-ordering process orswapping process, adds the flag described above to the header or thelike, and turn off the flag.

In that case, for example, the three-dimensional data decoding devicedecodes the flag described above from the header of the receivedbitstream. And the three-dimensional data decoding device performs there-ordering process or swapping process after performing the inversetransformation process if the flag is on, and does not perform there-ordering process or swapping process if the flag is off.

In this way, providing the flag in the bitstream allows thethree-dimensional data decoding device to properly determine whether thethree-dimensional data encoding device has given priority to improvingthe coding efficiency or reducing the processing amount.

Although an example has been described in which the three-dimensionaldata encoding device adds the re-ordering information or swappinginformation to the header or the like, the present disclosure is notnecessarily limited thereto. For example, the three-dimensional dataencoding device may encode the re-ordering information or swappinginformation as new attribute information (attribute) on thethree-dimensional points. Specifically, the three-dimensional dataencoding device encodes, as attribute information, ordering informationon the three-dimensional points yet to be subjected to the re-orderingprocess or swapping process. The three-dimensional data decoding devicecan recognize the ordering information on the three-dimensional pointsyet to be subjected to the re-ordering process or swapping process bydecoding the ordering information, which is attribute information, andtherefore can recover the original ordering of the three-dimensionalpoints in the point cloud data having been subjected to the re-orderingprocess or swapping process.

As described above, the three-dimensional data encoding device accordingto this embodiment performs the process shown in FIG. 114.

FIG. 114 is a flowchart of an encoding process according to thisembodiment.

First, the three-dimensional data encoding device re-orders the dataorder of a plurality of pieces of attribute information on a pluralityof three-dimensional points arranged in a predetermined order (S7461).For example, the three-dimensional data encoding device obtains pointcloud data including a plurality of pieces of attribute informationarranged in a predetermined data order, and re-orders the plurality ofpieces of attribute information on the plurality of three-dimensionalpoints in the obtained point cloud data in the predetermined proceduredescribed above.

The three-dimensional data encoding device then encodes the re-orderedpieces of attribute information on the plurality of three-dimensionalpoints in the re-ordered data order (S7462). Specifically, thethree-dimensional data encoding device encodes the pieces of attributeinformation on the plurality of three-dimensional points re-ordered instep S7461 in the re-ordered data order. For example, as describedabove, the three-dimensional data encoding device modifies the order ofthe pieces of attribute information on the plurality ofthree-dimensional points arranged in a Morton order so that thedistances between the three-dimensional points become smaller, andperforms the encoding process on the current three-dimensional points tobe encoded in the modified order.

The three-dimensional data encoding device then generates a bitstreamincluding order information that indicates the predetermined order andthe encoded attribute information on the plurality of three-dimensionalpoints (S7463). The order information is information that indicates thedata order of the pieces of attribute information on the plurality ofthree-dimensional points in the point cloud data input to thethree-dimensional data encoding device, for example. For example, thedata order of the pieces of attribute information on the plurality ofthree-dimensional points in the input point cloud data is a Mortonorder, the attribute information may be information that indicates thedata order is a Morton order.

With such a configuration, when the three-dimensional data encodingdevice encodes pieces of attribute information neighboring in the datasequence based on the differences between the values indicated by theneighboring pieces of attribute information, the three-dimensional dataencoding device can reduce the difference by modifying the order of thepieces of attribute information on the plurality of three-dimensionalpoints so that the pieces of attribute information indicating closevalues are adjacent to each other before encoding the pieces ofattribute information on the plurality of three-dimensional points.Therefore, the three-dimensional data encoding device can improve thecoding efficiency.

For example, in the re-ordering process (S7461) described above, thethree-dimensional data encoding device calculates distances between aplurality of three-dimensional points based on the geometry informationon the plurality of three-dimensional points, and re-orders the dataorder of the pieces of attribute information on the plurality ofthree-dimensional points based on the calculated distances.

When the attribute information is a value that indicates color forexample, attribute information on a three-dimensional point is likely tohave a value that is closer to the value of attribute information onanother three-dimensional point located near the three-dimensional pointthan to the value of attribute information on another three-dimensionalpoint located far from the three-dimensional point. Therefore, thethree-dimensional data encoding device can further improve the codingefficiency.

For example, in the re-ordering process (S7461) described above, thethree-dimensional data encoding device determines a reference pointamong a plurality of three-dimensional points, and modifies the dataorder so that the three-dimensional point closest to the reference pointamong the first to k-th three-dimensional points (k: an integer equal toor greater than 2) from the reference point chosen in a predeterminedorder is located next to the reference point.

With such a configuration, if k is properly set, the three-dimensionaldata encoding device can improve the coding efficiency without comparingthe distances between a vast number of three-dimensional points, thatis, without increasing the processing amount.

For example, the three-dimensional data encoding device arranges piecesof attribute information on a plurality of three-dimensional points in aMorton order, which is a predetermined order, based on geometryinformation on the plurality of three-dimensional points, and designatesthe three-dimensional point having the smallest Morton code value as areference point in the re-ordering process (S7461) described above.

With such a configuration, the three-dimensional data encoding devicecan modify the order of the pieces of attribute information on aplurality of three-dimensional points to a Morton order based on thegeometry information on the three-dimensional points, and properlyre-order the pieces of attribute information on the three-dimensionalpoints.

For example, the three-dimensional data encoding device includes aprocessor and a memory and the processor performs the process describedabove using the memory.

The three-dimensional data decoding device according to this embodimentperforms the process shown in FIG. 115.

FIG. 115 is a flowchart of a decoding process according to thisembodiment.

First, the three-dimensional data encoding device obtains a bitstreamincluding encoded pieces of attribute information on a plurality ofthree-dimensional points and order information that indicates apredetermined order, the encoded pieces of attribute information beingobtained by re-ordering the data order of pieces of attributeinformation on the plurality of three-dimensional points originallyarranged in the predetermined order and then encoding the pieces ofattribute information on the plurality of three-dimensional points inthe re-ordered data order (S7471). For example, the three-dimensionaldata decoding device obtains such a bitstream from the three-dimensionaldata encoding device.

The three-dimensional data decoding device then decodes the encodedpieces of attribute information on the plurality of three-dimensionalpoints in the data order of the encoded pieces of attribute informationon the plurality of three-dimensional points (S7472).

With such a configuration, the three-dimensional data decoding devicecan properly decode the attribute information on the three-dimensionalpoints encoded with improved coding efficiency.

The three-dimensional data decoding device then orders the decodedpieces of attribute information on the plurality of three-dimensionalpoints in the predetermined order based on the order information(S7473). The order information is the re-ordering table described above,for example. When the predetermined order is a Morton order, thethree-dimensional data decoding device may decode the encoded geometryinformation included in the bitstream, and arrange the plurality ofthree-dimensional points in the Morton order based on the geometryinformation.

The three-dimensional data decoding device then outputs the orderedpieces of attribute information on the plurality of three-dimensionalpoints (S7474).

With such a configuration, the three-dimensional data decoding devicecan modify the data order of the decoded pieces of attribute informationon the three-dimensional points to the data order of the pieces ofattribute information on the three-dimensional points before theencoding and decoding based on the order information, for example.Therefore, for example, equipment that has obtained the pieces ofattribute information on the three-dimensional points decoded by thethree-dimensional data decoding device can handle the pieces ofattribute information arranged in the same data order as those beforethe encoding and decoding.

For example, in the ordering process (S7473) described above, thethree-dimensional data decoding device calculates the distances betweenthe plurality of three-dimensional points based on the geometryinformation on each of the plurality of three-dimensional points, andorders the pieces of attribute information on the plurality ofthree-dimensional points based on the calculated distances.

When the attribute information is a value that indicates color, forexample, attribute information on a three-dimensional point is likely tohave a value that is closer to the value of attribute information onanother three-dimensional point located near the three-dimensional pointthan to the value of attribute information on another three-dimensionalpoint located far from the three-dimensional point. Therefore, forexample, the three-dimensional data encoding device can further improvethe coding efficiency by modifying the data order based on the distancesbetween the three-dimensional points and encoding the pieces ofattribute information on the three-dimensional points in the modifieddata order. That is, with such a configuration, the three-dimensionaldata decoding device can properly decode pieces of attribute informationon three-dimensional points encoded with further improved codingefficiency.

For example, in the ordering process (S7473) described above, thethree-dimensional data decoding device orders the pieces of attributeinformation on the plurality of three-dimensional points in thepredetermined Morton order based on the geometry information on theplurality of three-dimensional points.

With such a configuration, the three-dimensional data decoding devicecan properly arrange the pieces of attribute information on thethree-dimensional points by re-ordering the pieces of attributeinformation on the three-dimensional points in the Morton order based onthe geometry information on the three-dimensional points.

For example, the three-dimensional data decoding device includes aprocessor and a memory, and the processor performs the process describedabove using the memory.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data encoding method of encoding three-dimensional points, the method comprising: re-ordering, in a re-ordered data order, pieces of attribute information of the three-dimensional points arranged in a predetermined order; encoding the pieces of attribute information re-ordered in the re-ordering, in accordance with the re-ordered data order; and generating a bitstream including (i) order information indicating the predetermined order and (ii) the pieces of attribute information encoded in the encoding.
 2. The three-dimensional data encoding method according to claim 1, wherein the re-ordering includes: calculating a distance between the three-dimensional points in accordance with pieces of geometry information each included in a corresponding one of the three-dimensional points, and the re-ordering is performed in accordance with the distance calculated in the calculating.
 3. The three-dimensional data encoding method according to claim 2, wherein the re-ordering further includes: determining a reference point from among the three-dimensional points; and changing the predetermined order to the re-ordered data order in which a three-dimensional point having the distance shortest to the reference point among k three-dimensional points counted from the reference point to a k-th three-dimensional point in the predetermined order is at a position next to the reference point, k being an integer greater than or equal to
 2. 4. The three-dimensional data encoding method according to claim 3, further comprising: ordering the pieces of attribute information of the three-dimensional points to a Morton order that is the predetermined order, in accordance with the pieces of geometry information included in the three-dimensional points, wherein in the determining of the reference point in the re-ordering, a three-dimensional point having a smallest value of a Morton code among the three-dimensional points is determined as the reference point.
 5. A three-dimensional data decoding method of decoding three-dimensional points encoded, the method comprising: obtaining a bitstream including (i) pieces of attribute information of the three-dimensional points encoded and (ii) order information indicating a predetermined order, the three-dimensional points encoded being generated by (i) re-ordering, in a re-ordered order, the pieces of attribute information of three-dimensional points not yet encoded and arranged in the predetermined order and (ii) encoding, in accordance with the re-ordered data order, the pieces of attribute information re-ordered in the re-ordering; and decoding the pieces of attribute information of the three-dimensional points encoded, in accordance with the re-ordered data order.
 6. The three-dimensional data decoding method according to claim 5, further comprising: ordering the pieces of attribute information decoded in the decoding and arranged in the re-ordered data order to the predetermined order in accordance with the order information; and outputting the pieces of attribute information ordered to the predetermined order in the ordering.
 7. The three-dimensional data decoding method according to claim 6, wherein the ordering includes: calculating a distance between the three-dimensional points in accordance with pieces of geometry information each included in a corresponding one of the three-dimensional points, and the ordering is performed in accordance with the distance calculated in the calculating.
 8. The three-dimensional data decoding method according to claim 6, wherein in the ordering, the pieces of attribute information of the three-dimensional points is changed from the re-ordered order to a Morton order that is the predetermined order, in accordance with pieces of geometry information included in the three-dimensional points.
 9. A three-dimensional data encoding device that encodes three-dimensional points, the device comprising: a processor; and memory wherein using the memory the processor: re-orders, in a re-ordered data order, pieces of attribute information of the three-dimensional points arranged in a predetermined order; encodes the pieces of attribute information re-ordered in the re-ordering, in accordance with the re-ordered data order; and generates a bitstream including (i) order information indicating the predetermined order and (ii) the pieces of attribute information encoded in the encoding.
 10. A three-dimensional data decoding device that decodes three-dimensional points, the device comprising: a processor; and memory, wherein using the memory, the processor: obtains a bitstream including (i) pieces of attribute information of the three-dimensional points encoded and (ii) order information indicating a predetermined order, the three-dimensional points encoded being generated by (i) re-ordering, in a re-ordered order, the pieces of attribute information of three-dimensional points not yet encoded and arranged in the predetermined order and (ii) encoding, in accordance with the re-ordered data order, the pieces of attribute information re-ordered in the re-ordering; and decodes the pieces of attribute information of the three-dimensional points encoded, in accordance with the re-ordered data order. 